Why Dummy Tables are Smart!  A Systematic Approach to Data Analysis for Your M.Sc. Thesis

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Why Dummy Tables are Smart!  A Systematic Approach to Data Analysis for Your M.Sc. Thesis. Lisa Fredman, Ph.D. Department of Epidemiology, BUSPH CREST Seminar March 17, 2009 . Outline : 1. Research fundamentals (the basics) 2. Analytic plan in research a. Hypothesis guides plan

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Why Dummy Tables are Smart!

A Systematic Approach to Data Analysis for Your M.Sc. Thesis

Lisa Fredman, Ph.D.

Department of Epidemiology, BUSPH

CREST Seminar

March 17, 2009

Outline:

1. Research fundamentals (the basics)

2. Analytic plan in research

a. Hypothesis guides plan

b. Identify measures for E, D, and covariables

c. Descriptive statistics on E, D, and covariables

d. Analyses on E-D association

i. Crude analyses

ii. Evaluate potential confounders

iii. Multivariable analyses

3. Present results in tables and text

Aim: describe how dummy tables used in Steps 2a-d, 3

Research fundamentals:

- systematic investigation of E-D association

- analysis follows sequential steps from

descriptive analyses -> univariate E-D association -> confounder assessment -> multivariate modeling

- document methods and variables

- document analytic steps, results at each step,

decisions that influence next steps

- clear communication throughout

- hypothesis

- methods

- analytic steps

- results

Dummy tables

Definition: Dummy tables (aka mock tables) are shells of tables with variable names, SAS names, and statistical measures. Do not include data.

• Create dummy tables when develop analysis plan.
• Fill in dummy tables as perform analyses.
• Use dummy tables to guide analyses
• record SAS programs used for analyses
• names of measures used
• document interim results
• draft methods and results

Example of generic dummy table:

Brief notes on results, decisions, next steps

Why are dummy tables smart?

Stay focused on analyses to test YOUR hypothesis.

Provides template for systematic steps in your analysis.

Internal documentation.

Centralized record of analyses, results, decisions.

Communication aid.

Dumb things that smart researchers often do:

Analyze associations that look interesting but are tangential to their hypothesis.

DON’T BE TEMPTED TO DO THIS!

Revise analytic variables and not rename vars or record changes.

DON’T LET YOURSELF FALL INTO THIS TRAP!

Guide to dummy tables for analyses for epidemiologic study:

Before starting analyses:

• Write down hypothesis
• Make dummy table for each stage of analysis
• Make note to write summary of table, decisions, next steps.

• Descriptive analyses: variable distributions
• Crude analyses
• Bivariate analyses
• Confounder analysis
• Multivariable analyses

While doing analyses, at each step:

• Fill in dummy table and/or checklist at each stage
• Make decisions based on analyses at this stage (operationalizing variables, selecting confounders, excluding variables from multivariate model) that will influence next stage
• Write each decision and rationale for it

Proceed to next stage

EX: Making Corned Beef with Cabbage dinner

Generic dummy table aka “Shopping List”

(Title)

(Variables)

Stop & Shop or Shaws?

Need subgroup analyses!

EX: Making Corned Beef with Cabbage dinner

Fill in shopping list!

(Title)

(Variables)

Make notes to improve recipe

LF: use fewer onions, more carrots

LF: definitely plan on 2 hrs!

Use less water

Another example: is positive affect associated with better recovery in physical functioning following hip fracture?

Main study hypothesis:

• Elderly hip fracture patients with high positive affect will show recovery in more ADLs, and in more mobility-related ADLs over 2-years following fracture than patients with low positive affect or depression.
Filled-in dummy table and summary:

Summary of age-adjusted analyses: Respondents with low positive affect (PA) reported the fewest ADL limitations at baseline, and those with depressive symptoms reported the most. On average, respondents in each affect category reported more ADL limitations at each interview following the fracture. On the KatzADL variable, the high PA group reported the fewest ADL limitations 2-months through 18-months post-fracture. However, there were no statistically significant differences between respondents with high and low PA.

Filled-in dummy table for confounder assessment:

Summary: Age and 1 or more medical conditions (medsum42) met the criteria as potential confounders. I will also include race in the multivariable models since it may turn out to be a confounder in the models of the KatzADL outcome.

Summary: In the multivariable model, positive affect and followup time were associated with the KatzADL score over time. Mean KatzADL scores were significantly lower (ie, less impaired) in respondents with high positive affect compared to those with depressive symptoms at months 12 and 18; there were no differences between respondents with high and low positive affect.

Additional records to supplement dummy tables:
• Data memos to co-investigators/self
• Footers and WORD file names with filename and date created/revised