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Lecture 1. Bangkok Scientific Writing Workshop 30 January - 10 February 2006. Introductions and Course Overview . Monday 30 January 2006. UCSF Participants. UCSF, University of California, San Francisco UCB, University of California, Berkeley. Participants. Participants.

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introductions and course overview

Lecture 1

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Introductions and Course Overview

Monday 30 January 2006

ucsf participants
UCSF Participants

UCSF, University of California, San Francisco

UCB, University of California, Berkeley

why publish
Why publish?
  • Ethical obligation to subjects and society
  • To have the greatest public health and clinical impact
  • Really understand your topic
  • Currency of academic research
  • Future grant applications
course objectives
Course objectives

By the end of the workshop, you will hopefully have:

  • A complete draft of paper:
    • <3000 words, 3 tables, 1 figure, 20 references
  • A template for writing future research papers
  • The experience of peer review
  • A timeline for submission and publication
sections of a research paper
Sections of a research paper
  • Introduction
  • Methods
  • Results
  • Discussion
  • Acknowledgments
  • References
  • Tables
  • Figures
  • Title page
    • Long title
    • Short (running) title
    • Authors
    • Affiliations
    • Correspondence
  • Abstract
  • Key words
structure of the course
Structure of the Course
  • 2 weeks in length
    • First week for lectures and drafting sections of paper
    • Last week for completing writing with mentors and for formal peer review
  • Assigned reading and writing sections to be completed each day
  • Peer review of each section first thing each morning in week 1
  • Individual mentoring, writing time, statistical consultation
    • Usually in the afternoon, and more time towards the end of the course
  • Peer review of full articles
    • Last two days of course
lecture topics week 1
Lecture topics- Week 1
  • Monday 30 January:
    • Course overview
    • Title, introduction, literature review, references
    • Group excercise – “Elevator test”
  • Tuesday 31 January:
    • Choosing a statistical test
    • Methods
  • Wednesday 1 February:
    • Results
    • Tables and figures
lecture topics week 11
Lecture topics- Week 1
  • Thursday 2 February:
    • Discussion
  • Friday 3 February:
    • Abstract
    • Authorship, title page, choosing a journal, instructions to authors, cover letter, submission
lecture topics week 2
Lecture topics - Week 2
  • Monday 6 February
    • The peer review process
  • Tuesday 7 February
    • Responding to reviewers’ comments
  • Wednesday 8 February
    • Draft manuscript is due at 4:00 PM
lecture topics week 21
Lecture topics - Week 2
  • Thursday 9 February
    • Peer reviews - Groups 1 and 2
  • Friday 10 February
    • Peer reviews - Group 3 and 4
    • Completed manuscript is due at 4:00 PM
    • Course wrap up and evaluation
    • Graduation
additional course activities
Additional course activities
  • Every day:
    • One-on-one work with advisors/instructors
    • Team writing
    • Individual writing
  • Statistical consultation and analysis
the research question

Lecture 2

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

The Research Question

Monday 30 January 2006

learning to summarize research study or question in 1 2 sentences
Learning to summarize research study or question in 1-2 sentences
  • Forces author to understand and synthesize all the important elements of the study
  • Valuable skill for communicating clearly with colleagues
  • Applicable format to describe a research proposal, a study underway, or one that is completed
examples
Examples

Describing a study already completed:

We present the results of a randomized controlled trial among HIV-uninfected Thai injection users that evaluated if a recombinant gp120 vaccine reduces the incidence of HIV infection.

Describing a proposal:

Using an observational longitudinal cohort design, we will determine whether HIV+pregnant women who take vitamin supplements have improved pregnancy outcomes, compared to women who are not taking supplements.

three elements of research summary statements
Three elements of research summary statements

1. Study design

  • Trial – randomized, controlled, blinded (or not)
  • Cohort – longitudinal, cross-sectional, double, retrospective
  • Other sampling designs – cross sectional consecutive, convenience, chart review

2. Subjects

Men, women, HIV infected/uninfected, place of recruitment (clinic, hospital, community, geographic area – India, Africa, US)

3. Primary variables

  • Predictor
  • Outcome
examples of research questions
Examples of research questions

We present the results of a year long randomized controlled trial(1. study design) among 3000 HIV-uninfected Thai injection drug users(2. subjects) that evaluated if a recombinant gp120 vaccine(3. predictor) reduces the incidence of HIV infection (3.outcome variables) .

Class to identify 3 components:

Using an observational longitudinal cohort design, we will determine whether HIV-infected pregnant women who take vitamin supplements have improved pregnancy outcomes, compared to women who are not taking supplements.

class introductions and examples
Class introductions and examples

Everyone please introduce themselves

WILL EACH PARTICIPANT PLEASE TELL THE CLASS:

Your name

A summary of your paper/study including the 3 main elements

Areas needing most work– yourgoals for workshop

getting started

Lecture 3

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Getting Started

Monday 30 January 2006

first get organised
First: get organised
  • The best papers are hinged on a primary finding and its significance
    • Identify and select which findings you want to present in this paper
    • Avoid including everything-- can write other papers to elaborate on other findings
    • Think of the “MPU”: minimal publishable unit
  • Know the literature -- be a scholar
second be familiar with the specific manuscript structure of journals
Second: be familiar with the specific manuscript-structure of journals
  • Obtain “Instructions for Authors” from the journal in which you wish to publish
    • Examples:
      • Journal of the Medical Association of Thailand
      • AIDS
      • Lancet
  • Read model papers:
    • From journal of where you’d like to publish your paper
    • On topic similar to your paper
third make an outline
Third: make an outline
  • Make an outline with major headings:
    • Introduction
    • Methods
    • Results
    • Discussion
  • Use subheadings for Methods and Results
  • Make lists of each major point to be addressed in the introduction and the discussion
  • Keep manuscript parts together in one electronic and hard copy file
example of an outline
Introduction (general to specific)

Men who have sex with men (MSM) exist in all countries and cultures

MSM are severely affected by HIV/AIDS

Prior research in developing world has focused mostly on Brazil and Thailand

More recently, studies conducted in India

However, almost nothing is know about MSM in Africa

We implemented a survey of MSM in Uganda to gauge their level of risk for HIV

Example of an outline
fourth start writing
Fourth: start writing
  • Fill in outline as sentences and paragraphs are written
  • Be concise
    • Short sentences, short paragraphs
    • Use sub-headings to keep organized
    • Shorter papers have better chance of publication
tips for writing well
Tips for writing well
  • Start each paragraph with a topic sentence
  • Flow: move smoothly between paragraphs
    • Thought of last sentence flows into thought of first sentence of next paragraph
  • Avoid clichés
    • “Important”, “Significant”
    • “More research is needed” (unless you specifically say what is needed)
tips for writing well1
Tips for writing well
  • Peer review at least once; twice is better
  • Re-write & re-write & re-write
  • At some point soon: “Out the door”
  • If rejected, re-submit:
    • Every paper has a home
    • Reviewers can be biased and capricious
    • So can editors
comments on general style use scientific english or thai
Comments on general style: Use scientific English (or Thai!)
  • Papers dont have to be bland or boring
  • Use concise language and sentences
  • Imitate writing and language conventions of the field (psychology vs. Clinical or public health writing)
  • Use active voice (active verbs) as much as possible
  • Stick to facts that can be documented, and avoid speculation
  • Avoid the use of “I”. Limited use of “we” is OK.
  • American vs. British conventions
    • Spelling—depends on the journal, be consistent
    • Laboratory values
in what order should an article be written
In what order should an article be written?
  • Results -- Put tables and data together first--
    • Use “working tables” to organize and understand data and relationships – too lengthy for publication, but useful for author
    • Helps to Identify primary 1-2 findings of the paper
    • Rule of thumb: 3 tables and 1 figure for publication

2. Writing up results: follow order of the tables and figure

    • Describe subjects, distribution of demographics, main variables and main outcome (“univariate analyses”)
    • Bivariate analyses: association of predictors with main outcome
    • Multivariate and longitudinal analyses
    • Elaborate upon single most important finding
    • Sub-analysis of important groups and potential biases
in what order should an article be written1
In what order should an article be written?

3. Methods

  • Matches how you got Results (no more, no less)

4. Discussion

  • Primary important finding clearly stated first – punch line
  • Relevant other findings, confirmation of other studies, enhancing causal inference
  • Surprising, contradictory, unexpected findings
  • Limitations
  • Public health implications (HIV prevention or care)
in what order should an article be written2
In what order should an article be written?

5. Introduction

If written last – allows you to lead reader appropriately

  • First identify the general issue (HIV epidemiology, prevention, care in Asia, Thailand)
  • Specific issue
  • What is missing in current knowledge
  • How this study will address holes in current understanding

6. References

  • 20 is usually sufficient
in what order should an article be written3
In what order should an article be written?

7. Title

  • Title should reflect single main finding, or main point of study, and should be interesting

8. Abstract

  • Usually written last; falls more easily into place once results and discussion are written
alternative order of writing
Alternative order of writing
  • Introduction first
    • Use background section of your research protocol
    • Use this system if you need to research the literature to understand importance or context of primary findings. Can help focus.
  • Methods early on
    • Easy to write if already known
    • Helps you to recall exactly what was done in the study – particularly important if you didn’t implement or design the study. Clear understanding of methodology and its limitations is important for interpretation of results
    • Write this section if still waiting for analyses to be completed
alternative order of writing1
Alternative order of writing
  • Results, table and figures
    • Always construct tables first, before writing
  • Discussion
  • This is the method we’ll use
tips for writer s block
Tips for writer’s block
  • Only work on a topic that you are interested in
  • Just start.
    • Start filling in easy pieces
    • Don’t worry how it looks at first. It’s always easier to edit
  • Stay here, no e-mail, no cell phones
  • Write incrementally, by sentence, by paragraph, by section
title title page introduction and references

Lecture 4

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Title, Title page, Introduction and References

Monday 30 January 2006

title page
Title page
  • Title (today)
  • Authors (more later)
  • Author’s affiliations
  • Corresponding author’s address
  • Word count text, word count abstract
  • Disclosures, conflicts of interest, funding, previous presentations
types of titles
Types of titles
  • Explanatory
  • Interrogatory
  • Declarative
  • Cute and Catchy
the explanatory title
The explanatory title
  • Says exactly how the study was done
    • Study design
    • Main outcome
    • Main predictor
    • Study population, site
  • Advantages
    • Most common, recognized, standard
    • Sometimes required by journal
    • Targets relevant audience with key title words
  • Disadvantages
    • Can be boring, long
examples of explanatory titles
Examples of explanatory titles

Descriptive

HIV voluntary counseling and testing and HIV incidence in male injecting drug users in northern Thailand: evidence of an urgent need for HIV prevention

Analytic

Lack of association between human immunodeficiency virus type 1 antibody in cervicovaginal lavage fluid and plasma and perinatal transmission, in Thailand.

Intervention

The efficacy of fluconazole 600 mg/day versus itraconazole 600 mg/day as consolidation therapy of cryptococcal meningitis in AIDS patients.

the interrogatory title
The interrogatory title
  • Poses the most important question
  • Advantages
    • Catches interest
    • Focussed
  • Disadvantages
    • You better answer the question in the paper!
    • Says little about the design
    • May not be allowed by journal
examples of interrogatory titles
Examples of interrogatory titles

HIV seroconversion among factory workers in Phnom Penh: Who is getting newly infected?

Is there a heterosexual HIV epidemic in the United States? (Note: this paper did not answer the question)

Are recent increases in sexual risk behaviour among older or younger men who have sex with men? Answer: Both. (Note: question and answer in title)

the declaratory title
The declaratory title
  • Says the main finding as a simple sentence
  • Advantages
    • No one misses the point
    • Interesting, provocative, focussed
    • Good for conference abstracts
  • Disadvantages
    • May not be allowed by journal
    • Invites disagreement
examples of declarative titles
Examples of declarative titles

HIV infection may adversely affect clinical response to chloroquine therapy for uncomplicated malaria in children

Deferral of blood donors with HIV risk factors saves lives and money in Zimbabwe

Low socioeconomic status is associated with a higher rate of death in the era of highly active antiretroviral therapy, San Francisco

the cute and catchy title
The cute and catchy title
  • Uses a pun, humor, or trendy term
  • Advantages
    • Catches attention, interesting, provocative
    • Good for conference abstracts
  • Disadvantages
    • Glib, flippant, sometimes in bad taste
    • May not be allowed by journal
examples of cute and catchy titles
Examples of cute and catchy titles

Cruising on the Internet highway

The gay 90s: a review of research in the 1990s on sexual behaviour and HIV risk among men who have sex with men

A tale of two futures: HIV and antiretroviral therapy in San Francisco

what kind of title is each one
What kind of title is each one?
  • Case-control study of risk factors for Penicillium marneffei infection in HIV-infected patients in northern Thailand (actual title)
  • What are the risk factors for Penicillium marneffei infection in HIV-infected patients in northern Thailand?
  • Contact with soil is a risk factor for Penicillium marneffei infection in HIV-infected patients in northern Thailand.
  • Good penicillin and bad penicillin: risk factors for Penicillium marneffei infection in HIV-infected patients in northern Thailand
class participation
Class participation
  • Volunteer to present your explanatory titles
  • Volunteer to present your interrogatory titles
  • Volunteer to present your declarative titles
  • Volunteer to present your cute and catchy titles
additional tips for titles
Additional tips for titles
  • Are the title and main research finding closely related?
  • Is the title objective in tone?
    • If not declarative, can you back it up?
  • Are special features of the study mentioned?
    • E.g., Randomized, population-based, unique population, new method
introduction
Introduction

Think of the Introduction as 4 sentences:

  • The general situation
  • The specific situation
  • The gap in our knowledge of the specific situation
  • What you did to fill the gap
introduction1
Introduction
  • Build 4 sentences into 3-4 paragraphs
  • 10-20 references
  • Progress from general facts to specific facts
  • End on how your study fits into progression:
    • Entirely new hypothesis
    • More rigorous methodology, higher order of study
      • RCT, Population-based
    • Special, new population
    • New measure, test
example of a 4 sentence introduction
Example of a 4-sentence introduction
  • General:
    • HIV/AIDS care and prevention are rapidly expanding in world, Asia, Thailand
  • Specific:
    • Surveillance data, usually ANC-based, guide planning of care and prevention programs
  • Gap:
    • Few routine data on men, non-pregnant women
  • How we fill gap:
    • We analyzed trends in HIV prevalence at VCT sites to assess usefullness as surveillance tool
expand to 4 paragraph introduction
Expand to 4 paragraph introduction
  • Introduction:
    • General: Cambodia has a high level of STIs
      • Estimated numbers of STI
      • Impact on HIV transmission
    • Specific: Low partner treatment hinders STI control
      • Partner treatment is standard part of STI management
      • Barriers to partner treatment
      • Little partner treatment is done in Cambodia
    • Gap: Few studies of client-centered partner notification in Asia
      • Hong Kong: Patient-delivered partner treatment
      • Vietnam: Physician counseling
    • How will filled gap: We conducted a controled trial of a single session client-centered partner notification counseling intervention
example of introduction
Example of Introduction
  • Class participation: Attempt 4-sentence Introduction of your paper
references
References
  • In the Internet age:
    • No longer need an exhaustive literature review for every paper
    • A scientific paper is not a doctoral dissertation!
    • Less need for bibliography-like references
    • Less need to “prove” you are a world’s expert:
      • But still need to show you understand the issues
    • 20 journal papers are usually sufficient (10 are even better)
references in the introduction
References in the Introduction
  • Specific facts, assertions, assumptions
  • Seminal studies
  • Review papers on topic
  • Model for your paper (be sure your study has not already been done)
  • 10 to 15 are usually sufficient
references in the methods
References in the Methods
  • Previous publications from the same study
    • Especially if more detailed
  • New or unique measures, lab tests
  • Previously validated questionnaires
  • New or complex theoretical models
  • Occasionally unusual statistical tests
  • 0, 1 or 2 are usually sufficient
  • NO REFERENCES IN THE RESULTS!
references in the discussion
References in the Discussion
  • Strengthen causal inference:
    • Consistency in studies with similar or different methods
    • Biological plausibility, coherence
    • Alternative explanations
  • Contradicting studies
  • 10 – 15 are sufficient (some already used in Introduction)
hierarchy of references
Hierarchy of references
  • Recent, peer-reviewed journal articles
  • Very recent conference abstracts
  • Guidelines (from respected institutions, WHO, UNAIDS, CDC)
  • Medical text books
  • Websites (from respected institutions, factual)
  • Reports (if easily obtained, official)
  • Dissertations (hard for others to access)
  • Newsletters, fact sheets, non-peer reviewed
cautious use of references
Cautious use of references
  • In press (add full reference before printing)
  • Personal communication (person, date)
  • Unpublished data by one of the co-authors
  • Unpublished data by someone else
avoid use of references
Avoid use of references
  • From popular press
  • Future publications:
    • Submitted by not accepted
    • Not yet submitted, in preparation
    • Not yet written
  • References you don’t have handy (you will be asked to cite fully)
  • References you have not read
additional tips for references
Additional tips for references
  • Don’t over do it:
    • Too many for one fact or common knowledge
  • Results section should not have references
  • Line references up with corresponding facts with the sentence
    • Otherwise in chronological order
  • Redo search if long lag in publication
format for references
Format for references
  • Vancouver style, most common for biomedical journalsvan Griensven F, Thanprasertsuk S, Jommaroeng R, et al. Evidence of previously undocumented epidemic of HIV in men who have sex with men in Bangkok, Thailand. AIDS 2005; 19:521-526.
  • Psychology journals tend to use different formats (AIDS and Behavior)
format for references1
Format for references
  • For now, use placeholder:
    • [Last name of first author, year; next, year]
    • E.g., [Baryarama, 2002; Kaharuza, 2003]
  • Number using journal format, do last!
    • Superscript.1-4,12
    • Bracket [1-4,12]. Parentheses (1-4,12).
    • Psychology journals (Barayama & Kaharuza, 2003).
getting ready to write the results section the elevator test
Getting ready to write the Results Section: The “elevator test”

Class participation—5 volunteers needed!

You get into the elevator with your boss. He or she asks:“What did you find in that research study you did?”

You have one minute before the two of you get off on his or her floor.

Explain the single most important finding of your study in one minute.

today s homework
Today’s homework
  • Outline:
    • Major sections, major sub-headings, main points
  • Medline literature search:
    • Find 5 - 10 key references
  • Draft:
    • Title
    • Introduction (as 3-4 sentences or 3-4 paragraphs)
additional activities today
Additional activities today
  • Lunch
  • Work with mentors/analysts and write for remainder of the afternoon
  • We will divide you into four peer review groups for tomorrow and give each person a partner
  • Please give your partner your title and a copy of your introduction section at the end of the day today
  • Please make six more copies of your title and introduction for tomorrow morning’s peer review
methods

Lecture 5

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Methods

Tuesday 31 January 2006

methods1
Methods
  • Describe how you did the study with enough detail for the reader to judge the study’s strengths and weaknesses
  • Can be used to repeat study if needed
methods2
Methods
  • Four key points to communicate:
    • Study design
    • Subjects
    • Measurements
    • Analysis
methods3
Methods
  • Do not present results
    • Exception: Pyschology literature sometimes presents recruitment and subject description in Methods
    • Assume our papers will be biomedical (include description of subjects in Results)
    • But describe recruitment in methods here
methods4
Methods
  • But, methods are linked to Results section
    • If you add data to the Results, then must be sure how data was collected is described in the Methods
    • If you decide not to add certain analyses in the Results, then drop description from Methods
      • E.g. If a lengthy clinical exam, and laboratory evaluation was part of the study, but you are not describing these results, you do not need to provide detail in the methods; they can be mentioned, however.
methods5
Methods
  • 500 – 750 words
  • 0, 1 or 2 references
    • Previous paper from same study
    • Validated questionnaire
    • New laboratory test
    • Complex theoretical model for behavioral studies
methods6
Methods
  • Sub-sections with headings recommended
  • Find model paper to follow sub-section headings
  • Outline each sub-subsection
  • Expand each sub-heading to a short paragraph
minimal methods sub headings
Minimal Methods sub-headings
  • Subjects
  • Measurements
  • Analysis
expanded methods sub headings
Expanded Methods sub-headings
  • Overall study design
  • Setting
  • Study subjects
  • Study procedures and measurements
  • Intervention (if any)
  • Laboratory methods
  • Data analysis
  • Ethical considerations
1 overall study design
1. Overall study design
  • Pivotal sentence
  • Think one sentence:
    • “We conducted an X study of the effect of Y on Z in a population of W in Q from year R to year S.” (sound familiar? Study description......)
  • This sentence may appear at end of Introduction, instead
1 overall study design1
1. Overall study design
  • Basic study designs:
    • Cross-sectional survey
    • Case-control study
    • Cohort study (longitudinal, retrospective, x-sectional or showing BL data only)
    • Trial – Randomized, controlled
    • Before-after study
  • Combinations (describe both)
  • Special cases (more later)
1 overall study design2
1. Overall study design
  • Elements to include:
    • Prospective or retrospective (usually understood by basic design)
    • Blinded
    • Randomized
    • Secondary analysis of data collected for other purpose
    • Descriptive, exploratory vs. hypothesis testing
    • Time frame – either mentioned specifially when study was conducted, and length of f/u
examples of overall study design
Examples of overall study design

“We analyzed trends in the prevalence of HIV infection over a 9-year period (1992-2000) from a large database of rural Thai VCT clients tested at 4 established sites.”

examples of overall study design1
Examples of overall study design
  • Class participation: Is anyone unclear about their study design?
2 setting
2. Setting
  • Geography
    • County, urban, rural
    • Describe typical demographics of setting
    • Chance for a more interesting literary description – i.e. What kind of a place is Chiang Rai, Bantei Meanchay, Phuket?
  • Facility
    • Hospital, clinic, VCT sites—private, public, large etc.
    • Can describe typical clientele
2 examples of settings
2. Examples of settings

Geography: “The setting of our study is Pnomh Penh, Cambodia, the country’s capital and largest city...”

Facility: “The Phuket and the Takua Pa Hospitals are tertiary care facilities located in southern coastal Thailand near the areas most heavily damaged by the tsunami of 26 December 2004.”

2 setting1
2. Setting
  • Setting may be included in Overall Study Design
  • Setting may be included in Study Subjects
3a study subjects
3a. Study subjects
  • Who are they?
  • Where do they come from?
  • How did you sample them?
  • How did you recruit them specifically?
  • How did you enroll them?
3a study subjects1
3a. Study subjects
  • Who did you enroll :
    • Inclusion criteria
    • Exclusion criteria
    • Don’t spell out as separate headings
  • Time frame if relevant
    • Season may matter
3b study subjects
3b. Study subjects
  • Sampling design
    • Random sample- how randomized
    • Consecutive, convenience
    • Sub-sample of larger study
    • Special sampling procedures
      • Venue-Day-Time
      • Respondent-Driven Sampling
3c study subjects
3c. Study subjects
  • Procedures for:
    • Initial contact, or recruitment—came in through clinic; recruited through peer outreach; responded to advertisement
    • Enrollment- may be different from place of recruitment; enrolment implies that they met inclusion criteria
    • Consent –informed, signed or not
    • (IRB approvals)
3d study subjects
3d. Study subjects
  • Special case:
  • Case-control subjects:
    • Describe larger population
    • Describe definition of cases
    • Describe selection of controls
    • Describe matching (or mention no matching)
3d study subjects1
3d. Study subjects
  • Special case:
  • Randomized controlled trial:
    • Describe larger population from which subjects drawn from
    • Randomization procedures
    • Blinding
      • Subjects
      • Interviewers
      • Researchers
      • Statisticians
4a measurements
4a. Measurements
  • Describes how data were collected
  • If a qualitative study, describe:
    • Type of interview (in-depth interviews, open-ended, semi-structured)
    • Focus group discussions: recorded, transcribed how many per group
    • How long it took, who performed them, and where
    • Confidentiality, names, etc.
4b measurements
4b. Measurements
  • If a quantitative study based on questionnaire data, describe the interview and questionnaire:
    • Interviewer-administered, self-administered, ACASI
    • How long did it take; where done
    • How developed – piloted, revised, translated, back translated?
    • Were any items standardized?
    • Was a follow-up questionnaire done – if so, differ?
    • Describe general questionnaire domains, for example: demographic characteristics, general health, HIV/AIDS knowledge and attitudies, risk behaviors
4c measurements
4c. Measurements
  • Clinical evaluation
    • Physical exam- by whom, of what, including any particular measurements?
    • Treatment provided?
  • Follow-up exam?
  • Include only those measurements that are ultimately presented in Results
4d measurements
4d. Measurements
  • Special situations
    • Data collected for other purposes, secondary analysis
    • Data abstraction procedures- for chart reviews; meta-analyses
    • Other data sources:
      • Census
      • Other studies
      • Assumptions of models
5 intervention
5. Intervention
  • Details of intervention as intended
    • Theory (e.g., behavioral)
    • Components, logistics
    • “Dose”, intensity of program
  • Control activities
  • Intervention and Control Activities may be separate sub-headings
  • Results may describe what was actually delivered
6 laboratory methods
6. Laboratory methods
  • Often separate sub-heading
  • Screening, confirmatory tests; may need to indicate parameters for a pos test (OD cut-off)
  • Manufacturer of tests:
    • Product name (Company name, City, State or Country)
  • References for new, experimental tests
  • Indicate where performed –which lab
7a data analysis statistical methods
7a. Data analysis, statistical methods
  • Discuss where and into what program data was entered; where stored; where analyzed
    • “Data were entered on site into Access, transferred to SAS, and evaluated for range and logic checks. The data were then transferred to the server at the TUC data management center for analysis using SAS version 9.1 (SAS Institute, Cary, NC).”
7b data analysis statistical methods
7b. Data analysis, statistical methods
  • Focus on primary analysis
  • Statistical tests in order of use Results
    • Univariate-
      • Distributions of variables were evaluated using means, SD, median, range,and proportions
    • Bivariate-
      • Differences in proportions were evaluated using chi-square tests, difference in means using t-test.
      • Odds ratios were calcuated with 95% confidence intervals using logistic regression
7c data analysis statistical methods
7c. Data analysis, statistical methods
  • Variables
    • Identify primary predictor variables – particularly if collapsed, composite variable
    • Identify primary outcome variable
    • Scales – whether Cronbach-alphas used; factor analysis; new scales created or modified
7d data analysis statistical methods
7d. Data analysis, statistical methods
  • Special analyses
    • Multivariate
      • Which variables were included and criteria – associated in bivariate analysis, and p<.10
    • Stratification
      • Male vs. female, young vs. old
    • Sub-group analyses
    • Analysis of potential biases
      • Participants vs. non-participants
      • Lost to follow-up vs. retained in longitudinal studies
7e data analysis statistical methods
7e. Data analysis, statistical methods
  • Additional considerations
    • Collapsing of variables, transformation
    • Power estimation prior to study (not common)
    • Consideration of statistical significance
      • P < 0.05
      • P < 0.01 if many comparisons
      • P < 0.1 for interactions, inclusion in model
8 ethical considerations
8. Ethical considerations
  • Also can be at end of Study Subjects or on cover page
  • Approval by IRBs
  • Special considerations:
    • Vulnerable populations (prisoners, minors)
    • Exempt or waiver of IRB (use of secondary data)
    • Waiver of informed consent
examples of ethical considerations simple
Examples of ethical considerations: simple

“Human subjects review boards in Cambodia and the United States approved the study protocol.” – too vague

Better: the IRB committees of the Cambodian National Institute of Public Health, the Centers for Disease Control and Prevention and UCSF reviewed and approved the study

examples of ethical considerations special population
Examples of ethical considerations: special population

“Subjects aged 15 to 18 years were considered emancipated minors and able to consent to the study. The protocol for this study was reviewed, approved, and monitored by the ethical committees of the Mahidol University and the University of California, San Francisco.”

examples of ethical considerations exempt
Examples of ethical considerations: exempt

“The analyses presented in this report consisted only of secondary unlinked data analysis; no contact with human subjects occurred.”

This statement would go at the end of the “Data Collection” collection

special methods situations find models to follow
Special methods situations (find models to follow)
  • Randomized Controlled Trials
    • Special structure (CONSORT)
  • Evaluation of diagnositic tests
  • Mathematical modeling
  • Secondary analysis of multiple data sources
special methods situations find models to follow1
Special methods situations (find models to follow)
  • Review papers
  • Meta-analysis and systematic reviews
  • Cost-effectiveness analysis
  • Non-biomedical journals
today s homework1
Today’s homework
  • Draft (revise):
    • Methods (minimum outline sub-headings)
    • Revise title pages and introductions as needed
    • Please give a copy of your methods section to your partner and have six additional copies ready for peer review tomorrow morning
statistical analyses of data

Lecture 6

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Statistical Analyses of Data

Tuesday 31 January 2006

steps in data analysis 1
Steps in data analysis-1
  • Data collection
  • Data entry
  • Data cleaning

These FIRST THREE STEPS ARE CRITICAL – your analysis will only be as good as the data that are collected- “garbage in, garbage out”

data cleaning
Data cleaning
  • Should be ongoing – from initial data entry through analysis
  • The earlier you clean your data, the better – can sometimes be too late
    • May need to recall the subject, talk to the interviewer or clinician, repeat lab tests
  • Primary tools for data cleaning are range and logic checks- can be automatic if programmed into data entry system
data cleaning1
Data cleaning
  • For each measure, examine the data by:
    • Range checks (accuracy) - be sure there are no impossible values)
      • Example – subjects with age of 99 years, 60 children etc.
    • Logic checks - do they data make sense
      • Look for discrepant results
      • Consistency of answers between interviewers
        • Example: subject initially circumcised on baseline visit; on f/u described as uncircumcised
        • 50 subjects marked as undergoing syphilis testing; have results for 53 subjects
data cleaning2
Data cleaning
  • Missing values
    • Look for missing values and missing files
    • What are reasons for missing values?
      • Subject/ client didn’t respond
      • Data weren’t recorded
      • Data weren’t entered
      • Something was lost – file, questionnaire etc.

If numbers of missing values are large – need to resolve; if numbers are small and data set is large – can leave for now

data cleaning3
Data cleaning
  • How many of you have “clean” data?
  • How have you determined that they are clean?
data set preparation
Data set preparation
  • “Freezing” the database - the “dataset” - for analysis - no more cleaning or data entry
  • Variable items should be labeled –not by questionnaire number; response codes should be labeled for ease of interpreting output
  • Variables may need to be reformatted, new variables created; create composite variables
  • Output should be labeled by date with a cover sheet indicating contents; file the output
topics to be covered 1
Topics to be covered - 1
  • Basic “descriptive” statistics
    • Types of variables (2):
      • Categorical (dichotomous)
      • Continuous variables
    • Proportions /percents – categorical
    • Means, medians – continuous
      • Standard deviations, range, confidence intervals
topics to be covered 2
Topics to be covered - 2
  • Basic “analytical” statistics
    • Differences in proportions, between groups
    • Differences in means, between groups
    • Incidence, and incidence rates
topics to be covered 3
Topics to be covered - 3
  • Measures of bivariate association
    • Relative risk
    • Odds ratios
    • Relative hazard
    • Correlation coefficients
    • Kaplan-Meier and survival curves
topics to be covered 4
Topics to be covered - 4
  • Statistical tests and when to use them (depending on the predictor and outcome variables)
    • Dichotomous predictor/ dichotomous outcome (difference in proportions)

Chi-square /z-statistic: differences in proportions

    • Dichotomous predictor/ continuous outcome (difference in means)

T-test, Fisher’s exact test: differences in means

    • Continuous predictor and continuous outcome

Linear regression

topics to be covered 5
Topics to be covered - 5
  • Meaning of a p-value
    • How to present p-values
  • Multivariable analysis
    • Stratification
    • Multivariate logistic regression
      • Statistical test of differences in association
      • Presentation of results
    • Multivariate linear regression
topics to be covered 11
Topics to be covered - 1
  • Basic “descriptive” statistics
    • Types of variables (2):
      • Categorical (dichotomous)
      • Continuous variables
    • Proportions /percents – categorical
    • Means, medians – continuous
      • Standard deviations, range, confidence intervals
first step in analysis descriptive statistics frequencies of variables
First step in analysisDescriptive statistics – frequencies of variables
  • Important initial step in determining how clean your data set is
  • Understanding your data
types of variables
Continuous

Quantitative intervals in order

Examples:

Number of sexual partners

Weight

Age

Categorical

Dichotomous (yes/no)

(dead/alive, AIDS)

Nominal (name) –no order

(raçe, marital status, occupation)

Ordinal (order)

WHO stages of HIV infection, levels of education)

Types of variables

Continuous variables can be either distributed symmetrically (normally) or asymmetrically

slide130
Descriptive statistics: Univariate analysis Frequency distributions of all potential variables of interest
  • Number of observations –missing values
  • For categorical/dichotomous variables:
    • Proportions in each category of response
    • Point prevalence, surveillance– 95% CI
  • For continuous variables
    • Mean, standard deviation
    • Median, range of values
    • 95% confidence interval
  • Create categorical variables as needed from continuous variables; create composite variables
how to describe the distribution of categorical dichotomous variables
How to describe the distribution of categorical (dichotomous) variables
  • Number of observations = 250
  • Proportions- percents
    • Yes = 175/250 = 0.7
    • No = 75/250 = 0.3

N=175

Number in each category

70%

N=75

30%

Response category

Fig. 1 Number of subjects who received VCT testing. N= 250

distribution of a theoretical continuous interval variable
Distribution of a theoretical continuous (interval) variable
  • Number of observations (no. of subjects) = 100
  • Mean # partners =?
  • Median = ?
  • Mode = ?
  • Range = ?

No. of subjects

How is the variable distributed? What is the relationship between mean, median, mode?

No. of lifetime sexual partners

Fig. Distribution of no. of lifetime sexual partners

meaning of terms
Meaning of terms
  • Mean (M) = average value
  • Standard deviation (s) measures the variation in the values around the mean (M), for (N) observations, estimates the population from which sample was taken:

SD=s = √S (yi-M)2/(N-1)

  • Standard error of the mean = s/√N

Indicates the variation in values around Mean in the sample; always smaller than SD, s

  • Confidence interval (e.g., ±95%) = M ± 1.96s
what descriptive statistics should be shown
What descriptive statistics should be shown?
  • Standard deviation (s) –show if you want to indicate the spread of the variable in the sample/population
  • Standard error (of the mean) = s/√N

Generally less used, unless want to show how “precise” the measurement is; or want to have a small SE – i.e. look good

  • Confidence interval (e.g., ±95%) = better way of showing precision of the estimate
confidence intervals are used to estimate precision of a result
Confidence intervals are used to estimate precision of a result

Descriptive results:

Means, proportions

Analytic results:

Relative risk, odds ratios

To compare RR, OR

The wider the confidence interval, the less precise the estimate; smaller – usually larger sample size

e.g.: Prevalence of HIV in population is

15% (95% CI: 1%,30%) vs 15% (95% CI: 11%,19%)

continuous variables non normal distributions skewed data
Continuous variables:Non-normal distributions, skewed data

Mean > median >mode

Skewed by data at higher values

Mode > median > mean

Skewed by data at lower values

mode

median

Mean

No. of subjects

No. of sexual partners

Pink – SF

Green - Eureka

Fig. No of MSM sexual partners last mo in

SF and Eureka; N=77

slide139
What descriptive statistics should be shown for non-normally distributed or asymmetrical continuous variables ?

Median, with range:

Range indicates the upper and lower values

N=77 subjects in study of MSM in Eureka

Median # partners = 3.0 (Range: 1-9)

Mean = less appropriate

Median, with 25-75% percentiles

Indicates that 50% of all values lie within this range, or 25% above, 25% below

topics to be covered 21
Topics to be covered - 2
  • Basic “analytical” statistics
    • Differences in proportions, between groups
    • Differences in means, between groups
    • Incidence, and incidence rates
bivariate analysis
Bivariate analysis
  • Comparison of distribution (%, mean) of predictor variables within outcome groups
  • Comparison of distribution (%, mean) of outcomevariables within predictor groups
prevalence of outcome in dichotomous predictor groups row
Prevalence of outcome in dichotomous predictor groupsRow %

Can describe differences in prevalence :

67% of those with HIV had malaria, compared to 25% of those without HIV.

differences in means or medians between 2 groups either predictor or outcome
Differences in means or medians between 2 groups(either predictor or outcome)

Among HIV+ subjects, mean # lifetime partners was 20, compared to HIV- subjects, in whom mean # lifetime partners was 5.

incidence
Incidence
  • Incidence density is the overall incidence of outcome in sample
  • ID = (a+c)/N
  • Incidence in exposed = a/a+b
  • Incidence in unexposed = c/c+d
incidence is a rate and is measured over time
Incidence is a rate and is measured over time
  • Denominators are standardized to person-years of exposure
    • (usually per 100 person-years)
    • Calculate total number of persons and total follow-up time for each

Overall incidence, incidence density = 5/200 person-years

= 2.5/100 person years

= 2.5%/ (person) year

topics to be covered 31
Topics to be covered - 3
  • Measures of bivariate association
    • Relative risk
    • Odds ratios
    • Relative hazard
    • Correlation coefficients
    • Kaplan-Meier and survival curves
strength of associations
Strength of associations
  • Need a way to indicate strength of association between predictor and outcome variables.
  • This can be estimated by:
    • Risk ratio
    • Relative risk
    • Relative risk for incidence (hazard)
    • Odds ratio
    • Correlation coefficient

Terms risk ratio, rate ratio, relative risk, relative hazard are often mixed and confused

risk ratio is the association between dichotomous variables in cross sectional studies
Risk ratio is the association between dichotomous variables in cross-sectional studies

Risk ratio = a/(a+c) b/(b+d))

Risk of predictor in outcome group ÷

risk of predictor in group without outcome

relative risk is the association between dichotomous variables in cohort studies and experiments
Relative risk is the association betweendichotomous variables in cohort studies and experiments

Relative risk = a/(a+b) c/(c+d)

Risk of outcome in group with predictor ÷

risk of outcome in group without predictor

relative risk
Relative risk
  • Estimates the magnitude of an association and is equivalent to the probability that the outcome will occur, given an exposure, compare to non-exposed persons

Prevalence in exposed group (Ie)

RR=

Prevalence in non-exposed group (Io)

RR=1 No association

RR>1 Risk of outcome increased

RR<1 Risk of outcome decreased (protective)

relative risk example
Relative risk – example

Relative risk: (100/150) / (25/100) = .67/.25 = 2.68 = 2.7

HIV+ persons have 2.7 x risk of malaria

than HIV negative persons.

relative risk used when evaluating incidence rates
Relative risk – used when evaluating incidence rates
  • Incidence in exposed = a/(a+b)
  • Incidence in unexposed c/(c+d)
slide158

Relative risk – used when evaluating

incidence rates

N=250 subjects, malaria measured over 1-year period, no drop-out

Relative risk malaria incidence = (100/150) per 150 person years ÷

(25/100) per 100 person years

= .45/.25

= 1.8

odds ratio is the association between dichotomous variables in case control studies
Odds ratio is the association betweendichotomous variables in case-control studies

OR = ad/bc

Odds of outcome among those with predictor =

number with outcome (a) ÷ number who don’t develop outcome (b)

Odds Ratio: Odds of outcome in those with predictor ÷

Odds of outcome in those without predictor =

(a/b) ÷ (c/d) = ad/bc

odds ratio
Odds ratio

Odds Ratio:

Cohort study Odds of outcome in those with predictor/

Odds of outcome in those without predictor =

(a/b) / (c/d) = ad/bc

Case control: Odds of predictor in those with outcome/

Odds of predictors in those without outcome =

(a/c) / (b/d) = ad/bc

interpretation of odds ratios
Interpretation of Odds ratios
  • OR = 1.0 No effect
  • OR >1.0 Effect, greater odds of outcome
  • OR <1.0 Effect, less odds of outcome
precision of measurements 95 confidence intervals
Precision of measurements -95% confidence intervals
  • Generally – indicates that if the study were repeated numerous times, 95% of the time, the true value would lie between these limits
  • Symmetrical around value for means and point estimates (proportions) (m ± 1.96s )
  • Not symmetrical around a values for RR, or OR (because calculated using log-values)

When to Use:

Means

Point estimates /proportions

OR -- also indicates statistical significance

RR -- also indicates statistical significance

correlation coefficients measure strength of association between two continuous variables
Correlation coefficients measure strength of association between two continuous variables
  • If relationship is linear:

r = slope x SDpredictor

SDoutcome

r = correlation coefficient

SD = standard deviations of predictor and outcome

r2 = variance or the proportion of spread in one variable that can be explained by other variable

  • Example: Family income is correlated with years of education. If r=0.9 and r2=.81, then 81% of spread in income (variance) can be explained by differences in education
  • The closer r2 is to 1.0, the stronger the association
more complicated types of analyses for longitudinal studies
More complicated types of analyses for longitudinal studies
  • Kaplan-Meier & survival analysis – time-to-event analysis
  • K-M shows time to death, or proportion still alive
  • Used when follow-up periods and drop out are different
  • Survival analysis – shows proportion of persons free of the event (death or disease) over time
kaplan meier analysis
Kaplan-Meier analysis

Proportion of total subjects remaining alive

Time, months

topics to be covered 41
Topics to be covered - 4
  • Statistical tests and when to use them (depending on the predictor and outcome variables)
    • Dichotomous predictor/ dichotomous outcome (difference in proportions)

Chi-square /z-statistic: differences in proportions

    • Dichotomous predictor/ continuous outcome (difference in means)

T-test, Fisher’s exact test: differences in means

    • Continuous predictor and continuous outcome

Linear regression

slide167
Statistical tests measure the probability that the observed association (between predictor and outcome) is not caused by chance alone
analysis of a dichotomous outcome variable by a dichotomous predictor variable
Analysis of a dichotomous outcome variable by a dichotomous predictor variable
  • Chi-squared or z-test is used for dichotomous predictors and outcomes, or for 2 x 3 (4,5) associations
  • Tests differences in proportions
  • Fisher’s exact test is used when the expected values in any cell are <5
analysis of a dichotomous predictor variable and a continuous outcome variable
Analysis of a dichotomous predictor variable and a continuous outcome variable
  • Analyze by t-test
  • Use the same test when you have a continuous predictor variable and dichotomous variable
  • i.e. used to test differences in means between categories
  • ANOVA – test differences between multiple means
analysis of an experiment trial
Analysis of an experiment/trial
  • Analyze like a cohort study with RR
  • Intentional to treat analysis
    • Most conservative analysis
    • Include all subjects assigned to a treatment or control group, including those who never received the intervention
  • Subgroup analysis
    • Subgroups should be identified before randomization
topics to be covered 51
Topics to be covered - 5
  • Meaning of a p-value
    • How to present p-values
  • Multivariate analysis
    • Stratification
    • Multivariate logistic regression
      • Statistical test of differences in association
      • Presentation of results
    • Multivariate linear regression
interpretation of p value
Interpretation of p-value
  • When you run a statistical test, you will obtain a p-value
  • p stands for probability
  • From statistical point of view, you are testing the “null” hypothesis or the probability that there is “no effect”
  • A p-value of 0.05, means that there is an approximately 5% chance that there is no effect, or that association that is seen was due to chance
p values and statistical significance
p-values and statistical significance
  • What p-values are “significant”
  • Is p=0.052 different from p=0.049?
  • p=0.05 is a convention
  • You can show actual p-values, because even if they are greater than 0.05, they can demonstrate that there is an association even if it is not “statistically significant”
  • For p<.01 you don’t show actual values
    • p<.001, p<.0001 adequate
p values and confidence intervals
p-values and confidence intervals
  • Confidence intervals (CI) can also show statistical significance of an effect size (such as RR, OR)
  • CI that includes the value 1.0, indicates that there is NO effect; p>.05
    • RR = 1, no effect; RR<1.0 (negative, protective effect); RR>1.0 ( positive effect)
slide179
Table 6.27 Univariate Predictors Not Associated with Lung Cancer (After Adjustment for Other Factors in Multivariate Models)

Which predictors are significant at p<.05 value?

inferring causality in observational studies
Inferring causality in observational studies
  • Just because an association is “statistically significant”, does not mean that there the predictor variable has caused the outcome (this is referred to as “causality”)
  • Example from class – association, causality?
inferring causality in observational studies1
Inferring causality in observational studies

Strengthens concept of causality:

  • Predictor variable precedes the outcome variable in time
  • Strength of association
  • Biological plausibility
  • Association observed in different studies with different designs
  • Strength of association increases as exposure to predictor increases (dose response)
subgroup analysis
Subgroup analysis
  • Associations may be stronger in subgroups – example stratify be gender/age/marital status etc.
  • Subgroup analyses are frequently the most interesting and show the associations most clearly
topics to be covered 52
Topics to be covered - 5
  • Meaning of a p-value
    • How to present p-values
  • Multivariable analysis
    • Stratification
    • Multivariate logistic regression
      • Statistical test of differences in association
      • Presentation of results
    • Multivariate linear regression
strategies for confounding variables
Strategies for confounding variables
  • Definition of confounding variable?
  • Examples:
confounding variables
Confounding variables
  • Definition of confounding variable?
    • Factor that is associated with both the predictor of interest and the outcome
  • Examples:
    • Gum chewing is associated with smoking, and therefore gum chewing appears to be associated with lung cancer. Actual relationship is between smoking and lung cancer
    • Age is associated with HIV infection. Older age is associated with greater number of sexual partners; age confounds the relationship between number of sexual partners and HIV infection.
strategies for confounding variables1
Strategies for confounding variables
  • In the analysis phase
    • Stratification – by confounder or variable
    • Statistical adjustments – through multivariate analysis
stratification
Stratification
  • Separate participants into strata (or subgroups) by potential confounding variables (e.g., smokers and non-smokers)
  • Advantages: can be done after data collection, is flexible and one can un-do stratification
  • Disadvantages: lack of power (due to size of subgroups) and it is necessary to have measured the co-variates of interest
statistical adjustment
Statistical adjustment
  • Variety of techniques can control for multiple confounding variables simultaneously
  • Includes techniques like multivariate regression
    • Linear regression when variables are continuous
    • Logistic regression when the predictor variable is continuous and the outcome variable is dichotomous
  • Permits the full use of continuous variables
  • Statistics (e.g., AOR—adjusted OR) are more or less difficult to understand
multivariate logistic regression
Multivariate logistic regression
  • Various types of logistic regression models
  • Multivariate logistic regression looks at the association of a particular predictor with the outcome, while simultaneously “controlling” for other predictors (while holding them constant)
  • Include :
    • Variables for which you almost always want to control – e.g. age
    • Include variables that are significantly associated with the outcome in bivariate analysis
    • Some variables will not remain significant in multivariate model
slide190
Table 6.27 Univariate Predictors Not Associated with Lung Cancer (After Adjustment for Other Factors in Multivariate Models)
more complicated analysis of longitudinal studies
More complicated analysis of longitudinal studies
  • Cox proportional hazards models
    • When using survival analysis, associations between predictors and outcomes are expressed in as hazards
  • GEE models – longitudinal models with multiple time points and measurements
summary
Summary
  • Examine the distribution of each variable individually
  • Analyze your primary hypothesis with bivariate analysis
  • Measure the strength of association
  • Calculate the statistics for the comparison (e.g., p value)
  • Control for confounding variables
results

Lecture 7

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Results

Wednesday 1 February 2006

general recommendations for results
General recommendations for Results
  • Follow the sequence of Tables and Figures
  • Follow the sequence of the Methods (or vice versa)
  • Use sub-headings if complex or many secondary analyses
  • Think five paragraphs
    • 1000 to 1250 words, 4 – 5 pages
results in five paragraphs
Results in five paragraphs
  • Study population: eligible and recruited (1a: follow-up if RCT or prospective study)
  • Key variables and primary outcome (univariate)
  • Primary hypothesis (bivariate)
  • Multivariate analysis (confounding factors, interactions)
  • Stratification, subanalyses, examination of biases, corroborative analyses
results paragraph 1 recruitment
Results paragraph 1: recruitment
  • Number of persons approached
    • Where
    • When
  • Number of persons eligible
    • Why any excluded
  • Number of persons enrolled
    • Reasons for refusal
  • Differences between cases and controls
  • Differences between intervention and control arms
results paragraph 1 recruitment1
Results paragraph 1: recruitment

Example:

“A total of 246,715 clients age 15 years or older had their first test performed at the 4 main branches over the period January 1992 to December 2000. From these clients, we excluded 44,974 who reported illness as a reason for testing to avoid selection bias due to higher prevalence of HIV infection (61% vs. 14%, respectively) and an increase in the proportion of such clients over time (14% to 19%). The proportions of clients excluded due to illness were similar across sites: 15% Kampala; 17% Jinja; 25% Mbarara; and 20% Mbale.”

results paragraph 1a follow up
Results paragraph 1a: follow-up
  • Use only for cohort studies and experiments
  • Number of persons lost to follow-up
  • Reasons for loss to follow-up
  • Any difference between those lost to follow-up and those who continued in the study?
  • Remember that follow up is part of both Newcastle-Ottawa and CONSORT criteria
results paragraph 1a follow up1
Results paragraph 1a: follow-up

Example:

“A total of 137 persons (50%) completed the follow-up interview. Overall follow-up did not differ by study arm assignment (p=0.22). Persons lost to follow-up in the intervention arm did not differ from persons lost to follow-up in the control arm with respect to gender, age, education, employment, marital status, and types of partners (all p values >0.05). However, persons who completed follow-up were more likely to be women than men (58% vs. 42%, p=0.008) and less likely to have regular sexual partners (43% vs. 55%, p=0.048)...”

results paragraph 2 describe key variables and primary outcome
Results paragraph 2: describe key variables and primary outcome
  • Univariate results (Table 1)
  • Focus written text on main findings
    • Relevant demographic characteristics
    • Describe any differences in cases vs. controls
    • Describe any differences in experiment vs. control (placebo)
    • Main outcome prevalence or incidence density (here or in paragraph 3)
results paragraph 2 describe key variables and primary outcome1
Results paragraph 2: describe key variables and primary outcome

Example:

“Of 201,741 clients meeting the inclusion criteria, 49% were female, and 71% were younger than 30 years of age (Table 1). About half of these clients were single, and about one quarter were seeking premarital testing...”

May also include in paragraph 2:“Overall, adjusted prevalence of HIV infection declined from 23% to 13%...”

results paragraph 3 associations with outcome
Results paragraph 3: associations with outcome
  • Bivariate associations with main outcome (Table 2)
  • Focus written text on significant findings
    • Statistically significant
    • Clinically significant
results paragraph 3 main associations with primary outcome
Results paragraph 3: main associations with primary outcome

Example:

“Overall, adjusted prevalence of HIV infection declined from 23% to 13%, with a decreasse from 17% to 9% among men (P < 0.001) and from 31% to 17% among women (P < 0.001) (Table 2)...”

Expansion to additional main findings:

...Among men, significant findings

...Among women, significant findings

...by individual vs. couples testing

...by site, etc.

results paragraph 4 multivariate analysis
Results paragraph 4: multivariate analysis
  • Independent associations with main outcome (Table 3)
  • Main hypothesis, single most important finding
  • Focus written text on ruling out confounders
results paragraph 4 multivariate analysis1
Results paragraph 4: multivariate analysis

Example:

“Table 3 shows the results of the multivariate analysis comparing partner notification outcome by study arm controlling for potential confounding by partner types, gender, age, and employment status. Subjects allocated to the intervention counseling were significantly more likely to notify any partner compared to those in the control arm (OR 4.1, 95% CI 1.3 – 13.2).”

results paragraph 5 sub analyses
Results paragraph 5: sub-analyses
  • Stratified analyses (Table 4)
    • Special sub-populations
    • Effect modification (interactions)
    • Focus written text on differences between sub-populations
results paragraph 5 sub analyses1
Results paragraph 5: sub-analyses
  • Temporal trends (Figure 1)
    • Focus written text on significant increases or decreases
  • Ruling out biases
  • Secondary aims
examples of sub analyses
Examples of sub-analyses
  • Class participation: What important sub-analysis might you include in your paper?
additional tips for results
Additional tips for results
  • Don’t mix Methods into Results
    • If you conduct a new analysis or sub-analysis, add into Methods
  • Don’t mix Discussion into Results
    • No interpretation beyond self-evident
    • (Also, don’t introduce Results into Discussion, go back)
  • Be clear and concise
  • Double check numbers, do they add up?
tables and figures

Lecture 8

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Tables and Figures

Wednesday 1 February 2006

tables why use tables
TablesWhy use tables?
  • Tables present your information at a glance -- often readers will look only at tables and not read the Results section
  • Therefore, a reader must be able to understand your study population and all your results (i.e. your paper) by looking at the tables.
  • Compared to the text in the manuscript, tables:
    • Present data more compactly
    • Allow for side-by-side comparisons of data
tables general approach
TablesGeneral approach
  • Follows same format as the Results section
  • Table 1: Descriptive characteristics of your population – distribution of demographic characteristics; can be divided by groups of interest (among males/females; HIV+/-; disclosure/nondisclosure). Even if descriptive characteristics not a major component of the paper, all readers want to know basic information about the population
tables general approach1
TablesGeneral approach
  • Complicated studies – particularly randomized trials – need an initial figure illustrating recruitment, proportion eligible, refused, dropped out, randomized, followed
  • Table 2: Initial bivariate results – relationships between predictors and outcome.
  • Table 3; More detailed bivariate relationships, or multivariate results
  • Table 4 or Figure: Multivariate, survival analyses etc.
  • Figures, graphs: use only if provide new information in a more visually dramatic or understandable manner.
tables general approach 2
TablesGeneral approach -2
  • Rule of thumb: if you have fewer than 5 or 6 pieces of information to present in a table, consider putting it in the text instead
  • On the other hand, do not use excessive detail or you will detract from your overall message
  • Clearly label the rows & columns to assist the reader—particularly for figures, clearly label x and y axis.
components of a table
Components of a table
  • Title
  • Row and column headings
  • The rows themselves
  • The data
  • Footnotes
table components outline table 8 1 descriptive title such as structure of a typical table
Table components - outlineTable 8.1. Descriptive title, such as “Structure of a Typical Table”.*

The table should make sense without the text.

table components the need for n
Table componentsTHE NEED FOR “N”

All tables must have:

A total N – in title, or elsewhere

Denominators must be evident – must be able to calculate values in table

If numbers don’t add up, need to explain

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Table titles should be descriptive enough to tell reader what will appear in the tableTable 8.2. Poor titles and better alternatives
slide219
HeadingsTable 8.3 Selected hemodynamic measurements (Mean +/- SD) at baseline and during follow-up in 58 subjects with hypertension
  • The headings should be informative; don’t make reader refer back the to the text. Use a brief description.
  • Column headings reflect the comparison of primary interest.
  • Column headings should be distinctive; use italics or bold.
  • Put units in parentheses (or separated by commas) immediately after row descriptions.
table formatting
Table formatting
  • Rules for table details will be determined by the journal --- look at tables published in the journal you have chosen for examples and follow that format.
  • Keep footnotes to a minimum; use only for essential details and abbreviations.
  • Order or number your footnotes from top to bottom and within a line, from left to right. Use these symbols *, †, ‡, §, ║,¶. Double these symbols if you need more **, ††, etc.
table formatting continued
Table formatting, continued
  • Put the percentage symbol (%) right next to the number if space permits, e.g. 25%.
  • Align the numbers in each column by using a centering tab function or centering the cells in the table layout.
  • Center the column headings over the columns.
  • Cite all the tables in the manuscript text.
  • Adding formatting details and niceties make it easier on the reviewer – always a good thing.
types of tables
Types of tables
  • Tables that list information – rather than data—e.g. Describing testing algorithms, treatment algorithms
  • Tables listing characteristics of sample
    • Distribution of characteristics--%, % in categories, mean, median
    • Distribution of variables can also be given in relationship to an outcome variable – and therefore be “bivariate” – provide more information in 1 table, rather than 2 tables
types of tables1
Types of tables
  • Tables showing associations of predictor and outcomes:
    • Row percents—if showing proportion of outcome within predictor categories
    • Column percents – if showing distribution of characteristics between outcome groups (intervention, control; trial; ca-control studies
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Tables of distribution of values and frequencyTable 8.5. Frequency of pathogens in 840 women with lower urinary tract infections.

Classroom Exercise: What are the problems with this table?

table 8 5 frequency of pathogens in 840 women with lower urinary tract infections
Table 8.5. Frequency of pathogens in 840 women with lower urinary tract infections.

Suggestions for revisions –can offset numbers and percentages

identify deficiencies in following table table 8 6 characteristics of the subjects
Identify deficiencies in following table:Table 8.6. Characteristics of the subjects.

Problems: 1. Vague title 2. No column headings 3. No Total N provided 4. Both male and female categories not needed 5. Decimal to 1/100th not needed 6. Why is shoe size included? 7. Decimal point for calories per mo not needed 8. Why calories per month? 9. Should values be given by gender rather than for whole sample?

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Suggested improved tableTable 8.6. Characteristics of the 1194 Subjects Enrolled in the Better Eating Trial (BET).

Text could read: similar numbers of men & women in study; 33% of subjects were over 65 years old; 25% were more than 10 kg above ideal body weight; most were free of chronic medical problems.

table 8 7 characteristics of the 1194 subjects enrolled in the better eating trial bet
Table 8.7. Characteristics of the 1194 Subjects Enrolled in the Better Eating Trial (BET).

If actual numbers really don’t matter, an acceptable alternative is to show only the percentages and the means.

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Stratify the subjects into groups if there are important differences between the groupsTable 8.8. Characteristics of 1194 subjects enrolled in the the Better Eating Trial (BET), by gender.

Differences should also be pointed out in the text:

Men were more than twice as likely to have a history of heart disease, and diabetes was 40% more common among women.

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Results from a randomized trial – stratify by study groupsTable 8.9. Characteristics of 1194 subjects enrolled in the the Better Eating Trial (BET), by randomization status

  • Percentages may be easier to follow especially if the numbers in each study group vary a lot.
  • Describe what differences/statistical test to which p-values refer
tables that compare groups
Tables that compare groups
  • When you compare groups you are presenting either of two types of information
    • The measurements or characteristics of the groups
    • The differences between the groups
  • You need to decide which is moreimportant because it will determinehow you design your table
slide233

Table 8.10. Demographic profile and relationship to HIV status among high risk men in Mumbai (N=1901, 15% HIV+ overall).

* p<0.05 for the difference HIV% between categories

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Table 8.10. Demographic profile and relationship to HIV status among high risk men in Mumbai (N=1901, 15% HIV+ overall).

* p<0.05 for the difference HIV% between categories

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When you want to emphasize the predictor variables themselves, give the column %Table 8.11. Characteristics 112 Subjects Enrolled in study of TB among patients in Mulago Hospital.

Clear that the 2 types of subjects, HIV+ and HIV- are different.

Only need a p-value to show differences statistically significant.

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Emphasis on the comparison between groups (e.g. in a randomized trial)Table 8.12. Effect of intensive vacuuming on pulmonary function at 6 months in the Vacuum Away Dust (VAD) study.

When emphasis is on the differences between the groups, also

need to know if the difference is significant, measure of the effect size (in this study the effect size is measured by differences between means), and how precise it is.

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Presenting multivariate resultsTable 8.13. Independent predictors of coronary heart disease among 2124 middle-aged subjects.

Use meaningful terms such as relative risk and provide units for the predictor values. Units sometimes need to be spelled out (e.g. current vs never smoker) and sometimes can be implied e.g. men compared to women.

what should be left out of a table
What should be left out of a table
  • Don’t include everything that was measured. Pick out the important items and make your point.
  • However, don’t make this determination just by what was statistically significant. This is misleading.
  • To avoid accusations of multiple-hypothesis testing, have a few pre-specified hypotheses and indicate what they are. Report on these.
checklist for tables
Checklist for tables
  • Is the title sufficiently descriptive without being too much/too long?
  • Do the rows and columns line up neatly? Is each column centered under its heading? Are there denominators for the column headings? Are the headings bolded or italicized? Do the row characteristics (predictor variables) have units?
  • Are there any unneeded data, repeated N’s, excessive precision, or ambiguous abbreviations? Ask yourself: Do I need it?
checklist for tables1
Checklist for tables
  • Do I need it in such glorious detail? Do I need to abbreviate it?
  • Is the meaning of every item obvious without referring to the text?
  • After you have completed all of your tables, ask yourself: Can two or more tables be combined?
  • Are all the tables cited in the text? Are they cited in order?
figures1
Figures
  • Why use figures?

“One picture is worth a thousand words”

  • But use caution and common sense
    • Figures are time consuming
    • Good at conveying overall effects but poor at conveying specific measurements
    • If details matter, use a table instead or put the exact values in the text --- figures can only show a few results
    • A poor figure is worse than no figure at all
common types of figures
Common types of figures
  • Photographs
  • Diagrams
  • Data presentations
  • Maps
photographs
Photographs
  • Never assume the reader will recognize anything in a photograph
  • Label everything that is relevant, using arrows, asterisks and common abbreviations
  • Unless the scale of the photograph is obvious, include a ruler or indicate the magnification or reduction in the figure’s legend
photographs1
Photographs
  • Photographs are relatively expensive to publish and hard to include in an electronic version of the paper
  • Make sure the photograph is really needed and adds to the paper
  • To check clarity of the photograph, photocopy it –assume it will be copied over and over as your paper is passed around
diagrams as figures
Diagrams as figures
  • Appropriate diagrams might include the flow of subjects in a study, complicated sampling schemes, or a genetic pedigree
  • But keep it simple, err on the side of simplicity rather than thoroughness
  • Use smaller fonts for the less important items or details
  • Consider getting professional help -- good desktop publishing skills can make a diagram look professional and clear
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As a general rule sampling schemes are displayed verticallyFigure 8.1 Sampling scheme for the study

2311 subjects contacted

691 ineligible

135 refuse

1485 agree to participate

RANDOMIZATION

735 intervention

750 control

36 die or

dropout

41 die or

drop out

699 complete study

709 complete study

slide249

SUI with FSW

HIV/STI

aOR=1.4*

OR=2.2*

aOR=1.1

>10 FSW partners

aOR=1.7*

OR = 3.1*

Any unprotected

sex With FSW

OR=1.5*

aOR=0.8

Anal sex with

FSW

* p-value <0.05

OR: unadjusted OR of SUI to individual risk factors

aOR: adjusted OR from multivariableanalysis

Figure 8.2. Mediation analysis of relationship of sex under the influence of alcohol (SUI) to HIV/STIs

FSW = female sex worker

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Measurement algorithms can be displayed on a horizontal time axisFigure 8.3. Timing of study measurements

Initial interview;

Consent obtained

Repeat EKG

Repeat EKG

Exercise test

Enrollment

1992-93

First visit

March-June 1994

Second visit

Summer 1995

Study end

December 1996

figures that present numerical data
Figures that present numerical data
  • These types of figures are the hardest to do well. They can be very effective if done well, but need to ask if really needed.
  • Use if overall pattern is more important than actual values (1 picture worth 1000 words).
  • Figures should have a minimum of four data points. Anything less can be placed in the text.
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Figures connecting data points measured on several occasions in the same subjectFigure 8.4. Heart rate in beats per minute by day of treatment in 5 patients.

Multiple measurements on multiple occasions would be hard to demonstrate in a table or in the text.

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Effects in different groups or at different timesFigure 8.5. Differing effects of treatment with successolol in patients with low renin & high renin hypertension.

The filled diamonds are the means; the bars are the 95% confidence intervals

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Always check figures for potential mis-interpretation – Beware of lines that crossFigure 8.6. Blood glucose (red diamonds) and serum insulin levels ( yellow squares) versus time.

Note: eye is

drawn to crossover at day 3 & 4 even though just a coincidence

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Consider re-drawing with different scale to avoid problem of crossing linesFigure 8.6. Blood glucose (diamonds) and serum insulin levels (squares) versus time.

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Figures can be used to illustrate a lack of associationFigure 8.7. Lack of association between thyroid-stimulating hormone (TSH) and glucose in patients at weight-loss clinic

Bars jumping up and down, a tangle of lines or scattered dots can be effective, but be sure to indicate your interpretation – i.e. no association

r = -0.03

types of numerical figures
Types of numerical figures
  • Pie charts
  • Scatter plots
  • Bar graphs
  • Line graphs
pie charts figure 8 8 causes of neuropathy in primary care patients
Pie ChartsFigure 8.8. Causes of neuropathy in primary care patients.
  • Avoid using in written manuscripts
  • Effective for oral/powerpoint or poster abstract presentations
  • Data usually better presented in another format
  • Use text if only a few slices ; table if several slices or more

Can only use pie charts to show mutually exclusive categories

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Table 8.10. Gender of lifetime sexual partners among male participants in Mumbai intervention trial (N=1901).

Sex with females n=1892 (99%)

Sex with men n=431

(23%)

Sex with Females (n=1892)

90% FSW partner

35% Casual partner

60% Girlfriend

30% Married

MSF

n=1285

Sex with hires n=373

(20%)

Sex only with men n=11

(0.5%)

MSM

n=431

MSH

n=185

scatter plots figure 8 11 correlation between height and weight in 10 subjects
Scatter PlotsFigure 8.11. Correlation between height and weight in 10 subjects.
  • Scatter plots can easily show the correlation or lack of correlation between 2 variables
  • Showing the regression coefficient is helpful too

r = 0.76

bar graphs
Bar graphs
  • Valuable for displaying results by categories of subjects, e.g. men & women, or before & after
  • Use to provide more dramatic illustration of differences between groups
  • Most useful when the absolute value of the outcome variable is most important (rather than the confidence interval)
  • Need to choose how to display the pattern of the data
figure 8 12 likelihood of admission to an intensive care unit by age and gender
Figure 8.12. Likelihood of admission to an intensive care unit by age and gender
  • Results easily displayed in 2 dimensions
  • Compare values for men and women next to each other, for each age group
rearrangement figure 8 13 likelihood of admission to an intensive care unit by age and gender
RearrangementFigure 8.13. Likelihood of admission to an intensive care unit by age and gender.

Rearranged so that the taller bar stands to the right - visually easier to interpret

slide265
Too much rearrangement Figure 8.14. Likelihood of admission to an intensive care unit by age and gender.

3 dimensions:

age, gender and probability.

Looks

cluttered and is un-necessarily complicated.

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Crosshatches or lined bars to distinguish categoriesFigure 8.16. Annual risk of hepatoma by age and alcohol consumption.

Make sure patterns are clearly different – otherwise confusing; may be hard to distinguish; colors may be better but not many journals will print in color

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Can use colors to distinguish categoriesFigure 8.17. Annual risk of hepatoma by age and alcohol consumption

Colors are

effective for

PowerPoint presentations and posters; often cannot be used for publications

slide269
Stacked bar graphsFigure 8.18. Site of death among persons 65 years of age or older in the U.S and in Canada, 1988

When the pattern

is not clear, using

designs to

distinguish sections can be helpful. No more than four or five sections. Make sure patterns are different enough.

Stacked bar graphs may be confusing – be sure

to explain in legend and text

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When to use stacked bar graphsFigure 8.19. Proportions of U.S. graduating medical students in 1975, 1985, and 1995 choosing primary care specialties.

This is not so easy to read. Consider changing to stacked bar graphs --

work well when

the category totals

add to 100%

slide271
Figure 8.19. Proportions of U.S. graduating medical students in 1975, 1985, and 1995 choosing primary care specialties.

A stacked bar graph makes the point better than the previous bar graph –visually can compare each category more easily

line graphs figure 8 20 blood pressure in 10 subjects treated with ineffectivipine
Line GraphsFigure 8.20. Blood pressure in 10 subjects treated with ineffectivipine.

Reader will be confused into looking for a pattern

or hidden message when there isn’t one — except to show

no change. Might be useful for investigator to understand data.

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Survival CurvesFigure 8.21. Recurrence-free survival of cancer patients: intervention and control groups during 6-year follow-up.

Survival curves show proportion

surviving at various time points, also known as

Kaplan-Meier

Curves.

Intervention (N) 152 123 110 86 51 24 12

Control (N) 148 110 98 72 63 29 10

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Bar and Whisker PlotFigure 8.22. Mean, median, 25th and 75th percentiles, and range of creatinine clearance by age of the subjects.

Useful for describing the distribution of the data.

Shows the range (whiskers), mean (filled circle), median (horizontal line) and 25th & 75th percentiles (box).

slide275

Maps

Figure 8.23. Five year survival among persons with AIDS by census tract, San Francisco, California, 1996 - 2001.

Maps are useful to show geographic distribution of outcome variables

figure legends and text
Figure legends and text
  • Overcrowding is undesirable, but inadequate documentation is worse. Make sure your figure has a legend and that labels describe the x and y axes and the bars or lines in the figure.
  • Remember, figures in your article may be reproduced and used as a slide or handout by others.
  • The legend shouldn’t give away the results. The text should complement and expand on the information given in the legend.
  • Avoid ambiguous abbreviations; readers should understand the point of the figure at a glance.
checklist for figures
Checklist for figures
  • Is the figure necessary, helpful?
  • Does every figure make its point clearly? If not, have you tried alternative versions?
  • Are the axes, lines, bars and points labeled? Are the scales correct?
  • Does each figure have a legend?
  • Are the figures numbered and do they appear in the text in that order?
  • Does the text complement the information in the figures?
by end of today
By end of today
  • Revise Study Description and 2 Main findings (Not your title):
    • Study design
    • Subjects
    • Predictor and outcome

**********Analysis Plan*******

    • Obtaining data, cleaning data?
    • Running frequencies
    • Basic comparisons?
    • Sub-analyses
    • Multivariates?
today s homework2
Today’s homework
  • Prepare study description and main findings
  • Draft:
    • Primary tables and figures
    • Results section
    • Revise title page, introduction and methods as needed
study description and main findings not your title
Study description and main findings(not your title)
  • Study design, study subjects, outcome and predictors
    • Example: This is a cross-section study of risk factors for melioidososis among 100 survivors of the 2004 tsunami (could geographical site, part of larger study, etc.)
  • Main finding(s):
    • Melioidosis was diagnosed in 5% of survivors
    • Spending >2 hours in water was associated with risk of melioidosis (RR = 2.4, p=0.03)
discussion

Lecture 9

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Discussion

Thursday 2 February 2006

template for discussion
Template for discussion
  • Mission accomplished!
    • The single most important finding
  • Not only that…
    • Secondary findings
    • Confirms or refutes other published studies
  • Mea culpa
    • Limitations
    • But, redemption!
  • Wrap up and conclusions
    • Public health implications
mission accomplished
Mission accomplished!
  • The first sentence of Discussion
    • “We found…”
  • The “elevator test”
mission accomplished1
Mission accomplished!
  • The outcome of the RCT:
    • Did the intervention work or not?
  • The central hypothesis of your original proposal
  • Answer the question posed by “interrogatory title”
mission accomplished2
Mission accomplished!
  • Strengthen causal inference:
    • Rule out alternative interpretations
      • Chance (significance, large study)
      • Confounding (controlled for)
      • Bias (later, Mea Culpa)
    • Bradford-Hill’s criteria for causality
mission accomplished3
Mission accomplished!
  • Bradford-Hill’s criteria for causal inference (1965):
    • Cause before effect (cohort or cross-sectional?)
    • Biological plausibility (reason why? Theory, biology)
    • Consistency with other studies (triangulation)
    • Strength of association (magnitude of effect)
    • Dose-response (or, analogous dose-response)
    • Randomised controlled study
  • Does your study meet these criteria?
  • If not, do other studies?
examples of mission accomplished sentence
Examples of “Mission accomplished!” sentence

“Our assessment shows that despite Thailand’s remarkable success in controlling the HIV epidemic among the general population, the HIV prevalence among MSM was found to be surprisingly high.”

examples of mission accomplished sentence s
Examples of “Mission accomplished!” sentence(s)

“Thailand has a well-developed public health infrastructure that provides residents with more than 90% of their health care. The MOPH response to the December 26 tsunami was rapid and effective at mitigating the health consequences of the tsunami among survivors…Health assessments conducted 1 week after the tsunami indicated that, despite a huge influx in the number of patients, the medical system was intact and functioning effectively.”

class participation mission accomplished
Class participation: “Mission accomplished!”
  • Give a one sentence summary of your most important result….
not only that
Not only that…
  • “We also found…”
  • Sub-group findings, effect modifiers of single most important finding:
    • Men vs. women
    • Young vs. old
  • Secondary questions, findings
  • Unexpected findings (place in literature)
    • Contradict other studies, conventional wisdom
examples of not only that sentences
Examples of “Not only that…” sentences

“Consistent with research conducted in the the Western world, anal intercourse and increased sexual activity were the main risk factors for HIV infection.”

“As seen in other disasters, rapid health assessments can identify immediate health needs and help prioritize public health interventions.”

mea culpa
Mea culpa
  • “We recognize limitations of our study…”
  • Confess, come clean
    • No study is without potential bias
    • No study is perfectly executed
    • No study is definitive
  • Avoid criticism early on by acknowledging study limitations
  • Road to redemption
mea culpa1
Mea culpa
  • Start with single biggest threat to internal validity
    • Differential loss to follow-up
    • Participation bias
  • Explain (if you can):
    • Likely size of this bias
    • Likely direction of this bias
mea culpa2
Mea culpa
  • Address common problems and biases if they are a particular concern in your study:
    • Sample size, power (when no association)
    • Incomplete responses, data quality
    • Self-reported behavior, recall bias
    • Causality in cross-sectional study
    • Unmeasured and unknown confounders
    • External validity, representativeness
    • Alternative interpretations, explanations
    • Not enough money…
mea culpa and redemption
Mea culpa… and redemption!
  • “However, we do not feel this bias is likely to…”
  • How you did your best to address the bias in the design and analysis
  • Other evidence that bias is not likely to change primary conclusion
  • Other studies had worse biases
example of primary mea culpa sentences
Example of primary “Mea culpa” sentences

“Our findings are subject to several limitations. First, VCT clientele may not be representative of the general Thai population.”

“The primary limitation to interpreting our data is that only half of persons enrolled completed follow-up.”

examples of mea culpa sentences with redemption
Examples of “Mea culpa” sentences (with redemption)

“Second, our data were drawn from only 4 major towns in northern Thailand and do not represent the whole country. However, the fact that our data were comparable with ANC data suggests that our major findings of declining prevalence are not likely to have been affected significantly by such differences.”

wrapping it up
Wrapping it up
  • Don’t end on a sour note!
  • “Despite these potential limitations…”
  • Big picture, extrapolation
  • Public health implications
    • HIV prevention
    • HIV care
  • Clinical practice implications
  • Setting the future research agenda (be specific)
wrapping it up1
Wrapping it up
  • Sometimes the “wrap up” is added on as a separate section:
    • Conclusions
    • Recommendations
    • Program Implications
examples of wrap up sentences
Examples of “wrap up” sentences

“The high HIV prevalence found among MSM in Thailand coincides with reports of previously undocumented epidemics of HIV infection among MSM in China, Cambodia, and Indonesia and of ongoing HIV transmission among MSM in the Western world. The continuing spread of HIV among MSM highlights the urgent need for more effective behavioral and biomedical interventions to halt the spread of HIV infection in this population.”

examples of wrap up sentences1
Examples of “wrap up” sentences

“Despite limitations of our study, we believe that the addition of a single client-centered counseling session increased the delivery of partner notification services overall and improved self-reported success in referring partners to treatment.”

additional tips for the discussion
Additional tips for the Discussion
  • Stick to your data and your findings
    • Do not speculate on causes that are not suggested by your data
    • But, OK to offer new hypotheses
  • Do not include new study results
    • All findings must be in Results
    • Go back and include them
  • Avoid “More research is needed…”
    • Unless you say very specifically what is needed
additional tips for the discussion1
Additional tips for the Discussion
  • Do not simply repeat results
  • Use words rather than numbers or statistics
  • Avoid promising future papers or studies
additional tips for the discussion2
Additional tips for the Discussion
  • OK to strike uncertain tone if uncertain
  • Avoid bragging (well, a little is OK)
    • First ever, first in Asia, Thailand
    • First controlled study
  • Avoid clichés
  • Do not end on a sour note!
today s homework3
Today’s homework
  • Draft (revise):
    • Discussion section
    • Continue to work on other parts of your manuscript and analysis
abstract

Lecture 10

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Abstract

Friday 3 February 2006

general considerations for abstracts
General considerations for abstracts
  • Leave until last
    • Should fall into place if all other sections are done
    • Things change along the way
  • Abstracts for manuscripts are simpler than abstacts for conferences
  • Abstracts appear in MEDLINE
    • Make very easy to read
    • Numbers and statistics for key findings
general considerations for abstracts1
General considerations for abstracts
  • Check format for journal
  • Word count
    • 500, 250, sometimes as short as 100
  • Two types of abstracts
    • Unstructured
    • Structured
unstructured abstracts
Unstructured abstracts
  • No background or 1 sentence only
  • 1 sentence Methods (study design, study population)
  • 2-3 sentence Results (primary hypothesis, main findings, avoid Mea Culpa unless very big)
  • 1-2 sentence Discussion (main outcome [Mission Accomplished, possibly wrap up])
example of an unstructured abstract
Example of an unstructured abstract

“HIV voluntary counseling and testing (VCT), an important strategy for HIV prevention and care, has been available in all government hospitals in Thailand since 1992. We assessed factors associated with HIV testing, its uptake, and estimates of HIV incidence after HIV testing among male northern Thai injecting drug users (IDUs) admitted for inpatient drug treatment. Participants were interviewed about risk behaviors and HIV testing history before VCT was provided as part of the study. Of 825 IDUs who participated, 36% reported a prior HIV test. Factors associated with prior HIV testing in multiple logistic regression analysis included higher education and having >1 lifetime sex partner. Needle sharing was not associated with prior HIV testing. Of the 298 men with a prior test, 80% reported a negative result on their last prior HIV test, of whom 28% tested positive in our study, leading to an estimated incidence rate of 10.2 per 100 person-years. Fifty-nine percent of the IDUs who reported a prior HIV test stated that they did not receive pre- and/or posttest counseling. HIV incidence among IDUs remains high despite having VCT. Extending HIV prevention and harm reduction programs is urgently needed for IDUs in the region.”

Word count: 197

structured abstracts
Structured abstracts
  • Follow journal format
  • Basic:
    • Objective: not complete sentence
    • Methods
    • Results
    • Conclusion
  • Alternative: Conform to journal examples
    • Design, Setting, Main Outcome Measure
  • RCT format
example of an unstructured abstract1
Example of an unstructured abstract

BACKGROUND: The Ministry of Public Health (Thailand), MoPH, has had a program called National Access to Antiretroviral Program for People who have AIDS (PHA) or "NAPHA", to offer free antiretroviral drugs (ARV), which are locally produced in Thailand, to any HIV-1 infected patients with CD4<200 since 2002. This program may increase usage of ARV therapy and the emergence of HIV-1 drug resistance.

OBJECTIVES: To monitor HIV-1 ARV drug resistant codon mutation in Thailand before and after the "NAPHA" program.

MATERIALS AND METHODS: EDTA blood samples were collected from 542 HIV-1 infected subjects, who received ARV therapy in 1999 and 2001-2003, and perinatal chemoprophylaxis in 1998 and 2000. HIV-1 pol nucleotide sequences were analyzed. RESULTS: The percentage of drug resistant detection from the ARV therapy group in 1999 and 2001-2003 were 12.14 (34/280), 10.23 (9/88), 86.96 (20/23) and 57.55 (61/106), respectively. Of 332 NRTI drug resistant codon mutation, 226 (68.07%) were thymidine analogue mutations (TAMs). The percentage of TAMs detection in 1999 and 2001-2003 were 7.14 (20/280), 9.09 (8/88), 56.52 (13/23) and 43.34 (46/106), respectively. Of 105 NNRTI drug resistant codon mutation, 95 (90.48%) were related to nevirapine drug resistance.

CONCLUSION: Thailand may need more appropriate monitoring of drug resistance in the free ARV therapy program to protect the future usage of drugs by minimizing the emergence of drug resistance.

key words
Key words
  • Follow abstract
  • Select the 3 to 5 that will land your paper into the hands of the right audience
  • Standard words in MeSH headings
today s homework4
Today’s homework
  • Draft:
    • Abstract
    • Key words
    • Revise other parts of your manuscript
authorship

Lecture 11

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Authorship

Friday 3 February 2006

authorship1
Authorship
  • The “currency” of research
  • But, a source of hurt feelings
    • Recognition of collaborators
    • Cultural differences
authorship2
Authorship
  • Potential problems
    • Omission of those who merit authorship (or should have been offered the opportunity)
    • Inclusion of those who do not merit authorship
    • Order of authorship
  • Clarify authorship as early as possible
    • But, don’t stymie productivity
    • PI or mentor should shield you
authorship3
Authorship
  • Journals are cracking down
    • Frowning on non-contributors
    • Frowning on “ghost authors”
    • Frowning on “gift authorship”
  • Usually up to 6 authors acceptable
    • More than 8 may require written explanation
    • Some require written statement of roles
criteria for authorship
Criteria for authorship
  • International Committee of Medical Journal Editors
    • Established in 1978 in Vancouver
    • Established common criteria for publication of scientific articles in health
    • Established clear criteria for authorship in 1988
authorship criteria jama
Authorship criteria (JAMA)
  • Each author can swear, in writing:
    • Unique, previously unpublished
    • Can provide the data to publishers
    • Agree corresponding author can edit
  • Each author approves final manuscript
  • Each author:
    • Contributed to conception, design, analysis, interpretation
    • Put pen to paper, or major editing
    • Provided statistical expertise, obtained funding, logistical support, supervision
written justification of authorship
Written justification of authorship

“MH Katz participated in the planning and analysis of the data and wrote the paper. SK Schwarcz, TA Kellogg, and W McFarland participated in the planning and analysis of the data and edited the paper. J Klausner participated in the planning and analysis of the sexually transmitted disease data and edited the paper. JW Dilley participated in the planning and analysis of the anonymous testing data and edited the paper. S Gibson participated in the planning and analysis of the community survey data data and edited the paper.”

authorship rank
Authorship rank

Best: First and *corresponding = responsible for paper

2nd best:Last, “senior author”, PI, “grandfather of ideas”

3rd best: Second

4th best: Third, then drops off from here (only 3 authors then “et al” in many reference formats

5th best: Fourth and so on according to contribution

Worst: Next to last

*Corresponding author is responsible for paper: Can be anyone - Adds prestige, but responsibility

alternatives to authorship
Alternatives to authorship
  • Group authorship
    • Provides a means to add many authors
    • “…for the Young Men’s Survey Group”
  • Acknowledgements
    • For those who do not meet authorship criteria but who contributed
by end of today1
By end of today
  • Turn in to Sandy and Sanny - electronic copies
    • Study description above
    • Title
    • Introduction, Methods
    • Reference List
    • Drafts of Tables—Hard Copies Please
    • Do you have both Working Tables and Final Tables?
    • Draft Results section
    • Draft Discussion section
homework for weekend
Homework for weekend
  • By Monday
    • Make sure you have working tables
    • Revise tables for manuscript itself
    • Consider need for figures and prepare them – not everyone will need figures
    • Read section on statistical analysis – review. Consider what statistical tests and comparisons need to be done
    • Continue to revise all four sections as needed
timeline
Timeline
  • Next week
    • Monday Putting it all together
    • Choosing a journal
    • Tuesday – writing up the Discussion
    • Wednesday – longitudinal data analysis (David Glidden)
    • Thursday/Friday – Abstract, submission to journal, pick a journal
  • End of next week
    • Complete draft to be finished and turned in
putting it all together

Lecture 12

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Putting it all together

Monday 6 February 2006

choosing a scientific journal
Choosing a scientific journal
  • Field: Biomedical, psychological, social science, statistical
  • Focus: Disease-focused (e.g., AIDS) or general audience?
  • Audience: International or home country?
  • Competition: Competitive or very likely to be published?
  • Timing: Quick or long wait?
  • Sequence:
    • Aim high and go lower.
    • Or, go for the “sure thing”
  • Luck
choosing a scientific journal1
Choosing a scientific journal
  • Check the references section in your proposal to see what journals have published similar articles
  • Ask your preceptor, professors, boss
  • Check word count, length requirements
    • Full article of original research
    • Brief
    • Data letter
    • Letter to the editor
  • Sponsored supplements
biomedical general audience international highly competitive
Biomedical, general audience, international, highly competitive
  • New England Journal of Medicine
  • British Medical Journal
  • JAMA
  • Lancet
  • Science
  • Nature
  • PLoS Medicine
public health epidemiology infectious diseases very competitive
Public Health, epidemiology, infectious diseases, very competitive
  • American Journal of Public Health
  • American Journal of Tropical Medicine and Hygiene
  • American Journal of Epidemiology
  • Bulletin of World Health Organization
  • Clinical Infectious Diseases
  • Epidemiology and Infection
  • International Journal of Epidemiology
  • Journal of Infectious Diseases
  • Lancet Infectious Diseases (primarily reviews)
  • Sexually Transmitted Diseases
  • Sexually Transmitted Infections
  • Transactions of the Royal Society of Tropical Medicine and Hygiene
  • Tropical Medicine and International Health
other specialty journals
Other specialty journals
  • Pediatrics
  • Transfusion
  • Family planning, obstetrics and gynecology
  • Social science (Social Science in Medicine)
  • Free access journals (BMC Public Health, BMC Infectious Diseases)
  • Health Policy and Planning, Journal of Health Policy
  • Virology
southeast asian general medical and specialty journals
Southeast Asian general medical and specialty journals
  • Journal of the Medical Association of Thailand
  • Journal of Public Health (Bangkok)
  • Southeast Asian Journal of Tropical Medicine and Public Health
  • Southeast Asian Journal of Social Science
hiv aids focused journals
HIV/AIDS-focused journals
  • AIDS (number 1)
  • Journal of the Acquired Immune Deficiency Syndromes
  • AIDS and Behavior
  • AIDS Education and Prevention
  • AIDS Care
  • AIDS Research and Human Retroviruses
  • AIDS Patient Care and STDs
  • International Journal of Sexually Transmitted Diseases and AIDS
impact factor
Impact factor
  • Counting references to rank the use of scientific journals was reported in 1927 by Gross and Gross.
  • The term “impact factor” was used in Science Citation Index (SCI) in 1963.
  • This led to a byproduct, Journal Citation Reports (JCR), and a burgeoning literature using bibliometric measures.
impact factor1
Impact factor
  • Science Citation Index (SCI) is a publication related to Journal Citation Reports, publishing “impact factor” of journals since 1963.
  • The “impact factor ratio” is calculated as the number of citations in 1 year for all articles divided by the number of articles published in the journal in the last two years
106 articles published in 58 journals indexed in pubmed with thailand and hiv 2005
AIDS (10)

Southeast Asia Journal of Tropical Medicine and Public Health (8)

Journal of the Acquired Immune Deficiency Syndromes (7)

AIDS Care (6)

J Med Assoc Thailand (6)

American Journal of Tropical Medicine and Hygiene (3)

Antiviral Therapy (3)

Plus two each from:

AIDS Research and Human Retroviruses (2)

Health Care for Women International (2)

Health Policy (2)

International Journal of Epidemiology (2)

International Journal of Tuberculosis and Lung Disease (2)

Journal of Clinical Microbiology (2)

Pediatric Infectious Disease Journal (2)

106 articles published in 58 journals indexed in PubMed with Thailand AND HIV, 2005
cover letter
Cover letter
  • Accompanies manuscript to editor
  • Official letterhead
  • Address for correspondence
  • Increasingly done by e-mail
  • Consent form signed by all authors
cover letter 1 st paragraph
Cover letter: 1st paragraph

“Dear Dr. Patterson:

Please find enclosed four copies of a manuscript entitled “The role of the Mediterranean diet in the prevention of protease inhibitor-associated lipodystrophy in Croatia” for consideration for publication.”

cover letter 2 nd paragraph
Cover letter: 2nd paragraph
  • Paragraph 2: Why this paper will be of interest to your readers

“We feel the paper will be of particular interest to your readers as it addresses a unique experiment of nature that may have widespread clinical applicability. Few studies of protease inhibitor-associated lipodystrophy have been able to evaluate the diet of patients.”

cover letter additional
Cover letter: additional
  • Suggested reviewers and why (if allowed)
  • Not submitted elsewhere
  • Co-authors meet criteria
  • All co-authors sign (typically they will all sign a form that the journal provides)
  • “We hope our paper will receive favorable consideration for publication in AIDS.”
maximizing the chance of publication
Maximizing the chance of publication
  • Short
  • Clear, concise writing
    • Easier to accept one more short paper
    • Easier to communicate most important point
  • Conforms to normal conventions, format
    • Introduction, Methods, Results, Discussion
  • When returned for revision, careful, thoughtful, diplomatic response to every single peer reviewer comment
today s work homework
Today’s work, homework
  • Draft:
    • Cover letter to journal editor
    • Continue revisions of all sections
journal review and responding to reviewers comments

Lecture 13

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Journal review and responding to reviewers’ comments

Monday 6 February 2006

under review
Under review
  • Usually 2 reviewers, sometimes 3, plus editor
  • Online submission indicates status
    • Sent to review or not
  • If under review for greater than 4 months, contact journal
    • Reviewer late
    • Lost article
    • Editor can’t decide
types of comments from reviewers
Types of comments from reviewers
  • Major concern due to:
    • Flaws in design, analysis, interpretation
    • Confounding, often unmeasured
    • Evidence of other studies
  • Minor concern:
    • Include alternative view
    • Include key reference
  • Editorial, style, grammar
frustrating reviewers comments
Frustrating reviewers’ comments
  • Appear not to have grasped main point
  • Appear not to have carefully read
  • Read too carefully! Long comments
  • Not constructive
    • No suggestions for what do change
    • Nothing can be done
    • “The study the authors should have done…”
  • Pedantic, showing off
  • Have their own agenda
    • Consider appeal to editor
editor s decision letter
Editor’s decision letter
  • Never say: “Great, we’ll take it.”
  • “Favorably disposed pending minor revisions…”
  • “Cannot accept in present form, but will consider with revisions that address the reviewers’ comments…”
  • “Willing to consider resubmission with major revisions…”
  • “Unfortunately, your paper did not receive a high enough priority rating…”
responding to reviewers comments
Responding to reviewers’ comments
  • Resubmit if any positive response
  • Address every single point, number by number and say exactly how you have changed the text, table or figure
responding to reviewers comments1
Responding to reviewers’ comments
  • Concede all “easy points”
    • Additional references
    • Include alternative points of view
    • Grammar, style
  • Polite, contrite, diplomatic
  • Avoid rebuttals if possible
letter responding to reviewers comments
Letter responding to reviewers’ comments
  • Paragraph 1:

“We are delighted that AIDS & Behavior will consider publication of our paper pending satisfactory revisions as suggested by the reviewers.”

letter responding to reviewers comments1
Letter responding to reviewers’ comments
  • Paragraph 2:

“We have given careful consideration to all the reviewers comments and have done our best to address them all. The following is a point by point explanation of how we have address the concerns and revised our manuscript.”

letter responding to reviewers comments2
Letter responding to reviewers’ comments

Addressing Major Concerns:

Reviewer #1.

“1. The authors should address the question of whether HIV seroconversion is associated with amphetamine use or drug use in general.

“Following the reviewer’s suggestion, we constructed a variable for ‘any drug use’. Persons with any drug use had elevated risk for unprotected sex (RR=2.3, 95% CI 1.2 – 4.4) compared to non-drug users. For persons who used amphetamine (with our without other drugs) the association with HIV seroconversion was even further elevated (RR 3.0, 95% CI 1.4 – 6.5). These new results suggest that amphetamine use is more strongly associated with HIV seroconversion than drug use in general. We have added these results to page 13 as….”

letter responding to reviewers comments3
Letter responding to reviewers’ comments

Addressing Minor Concerns:

Reviewer #2.

“10. Finally, on a minor point, the authors speak of ‘amphetamine use during sex.” The phrase ‘sex during amphetamine use’ might be better.”

“We have changed the phrasing to ‘sex during amphetamine use’.”

letter responding to reviewers comments4
Letter responding to reviewers’ comments

Addressing Minor Concerns:

8. Page 3, line 12, we have deleted the word “seductively”.

9. Page 5, line 12 and References, we have added the citation by Mermin et. al. as #19 and made corresponding changes to the numbering.

letter responding to reviewers comments5
Letter responding to reviewers’ comments
  • Final paragraph:

“We thank the reviewers for their thoughtful comments. With these revisions, we feel the paper has been substantially improved. We hope it will receive favorable consideration for publication in …”

peer reviewing

Lecture 14

Bangkok Scientific Writing Workshop

30 January - 10 February 2006

Peer reviewing

Wednesday 8 February 2006

peer review purpose
Peer review: purpose
  • An organized process to give and receive constructive feedback
    • Early on for major modification before waste too much time
    • At end to maximize chance of publication
    • In this course we only have time for the latter
  • Better to find issues now among friends than later among critics
  • New ideas from a fresh perspective
peer review purpose1
Peer review: purpose
  • NOT: A test of reviewer’s ability to tear apart your work
  • NOT: A test of your ability to defend of what you did
  • NOT: A time to say, “The study you should have done is...”
format for reviewer
Format for reviewer
  • Start with a one sentence statement of the central finding of the paper
    • To be sure you understand the paper
  • Provide three strengths of the paper or study
    • Everyone is sensitive
  • Select three concerns or issue to discuss
    • Other minor points can be written on draft or discussed later
    • Address grammar and style by written comments
format for reviewers
Format for reviewers
  • Types of critique:
    • Things that appear incorrect (wrong analysis, internal contradictions, interpretation of findings)
    • Sections that are unclear
    • Sections that need more detail
  • With each major criticism, provide a concrete solution or suggestions
  • Reviewer 1: Limit verbal commentary 15 minutes
  • Reviewer 2: Don’t repeat, only add something new
format for author
Format for author
  • Listen
    • Resist the urge to defend what you did
  • Take notes and use what is helpful
    • Some ideas will be excellent
    • Sometimes, the reviewer missed the point (but so might other readers, clarify)
  • OK to disagree with reviewer
    • But, no need to argue
    • Silently reject and move on
when the author should speak
When the author should speak
  • Optional two minute introduction that calls attention to particular areas you are having problems with
  • Provide clarification only if the reviewer specifically asks
  • To ask for clarification of a comment
  • At end, to ask for more feedback on a specific area of difficulty
  • To thank the reviewer
format for chair
Format for chair
  • To keep track of procedures and time
  • To stop unproductive arguements
  • Perogative to comment on paper as well
  • Perogative to allow others present to comment
  • To summarize major points made
  • To thank both reviewer and writer
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