Why do clinical research
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Why Do Clinical Research - PowerPoint PPT Presentation

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Why Do Clinical Research?. Satisfaction of answering important questions which will improve the health of our patients Status of researchers Skill advancement Professional advancement Salary and Job Security. What is Research?.

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Why Do Clinical Research?

  • Satisfaction of answering important questions which will improve the health of our patients

  • Status of researchers

  • Skill advancement

  • Professional advancement

  • Salary and Job Security

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What is Research?

  • Research is the endeavor to discover new facts, procedures, methods, and techniques by the scientific study of a course of critical investigation

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Clinical Research

  • Clinical research involves working with human subjects to answer questions relevant to their well-being

  • Patient oriented research is where the ‘rubber meets the road’!

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‘How To Do’ Research

  • Start with defining the question

  • Write down a clear aim

  • Divide the problem into smaller, answerable questions

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‘How To Do’ Research

  • Develop hypotheses

  • Decide what data is needed to test the hypotheses

  • Refine the above and check the line of thought

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Good Research


    • Essential for both the problem and the answer


    • Exactness and precision come from hard work and responsible effort


    • If repeated will the answer be the same?

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Good Research


    • The researcher exposes all possible prejudices at the onset of the study design and strives to overcome them

    • Will the research be untarnished by personal gain, biases, vested interests, etc?

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Researcher Qualities

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Eager to learn







Researcher Qualities

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Getting Started

  • Learn your subject

  • Read, Read, Read

  • Start general and then focus

  • Begin with the problem

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Getting Started

  • Formulate the problem as a research question

  • Reduce the question to a single unambiguous question that is well-defined and answerable

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Stages in Creativity


    • Realize the need for a study


    • Gather relevant information


    • Think through the problem


    • Imagine possible solutions


    • Evaluate the solutions you have generated

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  • Thesis is the position that you believe represents truth

  • Hypothesis is the foundation on top of which you build your thesis

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  • Hypothesis is a tentative construct to be proved or disproved according to the evidence

  • The hypothesis is sometimes expressed as a null hypothesis

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A Good Hypothesis Should:

  • Be testable

  • Convey the nature of the relationship being tested

  • State exactly what variables form this relationship

  • Reflect all variables of interest

  • Be formulated early on in the planning stage

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Study Types

  • Will you test a hypothesis or describe a phenomenon?

  • Observational

    • Longitudinal

    • Cross-sectional

  • Randomized, double-blind, parallel group, placebo controlled trial

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Epidemiology vs RCT

  • Epidemiology allows the study of the real world and the development of hypothesis regarding disease states

  • Randomized, controlled trials allow the rigorous testing of hypothesis in a well characterized manner that is less real world in nature

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Study Design

  • Study Population

    • Age

    • Gender

    • Ethnicity/Race

    • Disease characteristics

    • Exclusions

    • Number

    • Stratification

    • Randomization

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Human Subjects

  • The safety and rights of human subjects must be protected

    • Study Design

    • Institutional Review Board

    • Informed consent

    • Data Safety Monitoring/Medical Monitors

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Key Questions

  • What is the main purpose of the trial?

  • What treatments will be used and how?

  • What is the participant risk?

  • What are the possible benefits?

  • How will patient safety be monitored?

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Key Questions

  • Are there alternative treatments?

  • Who is sponsoring the trial?

  • What is the participant burden?

    • How long and where?

    • What do the participants have to do?

    • Will there be any discomfort even if there is no risk?

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  • Define methods carefully

  • Decrease variability

  • Check reliability/reproducibility

  • Are you testing what you think you are testing?

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  • Try to ‘walk through’ the study and consider as many likely scenarios as possible.

  • Try to design in any variations in treatment or data collection that you think will occur before the study starts

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Operationalize Concepts

  • Specify how you will repeatably and reliably measure the variables you are using to answer the question

  • An operational definition specifies how your concepts will be observed and measured

  • This should allow your research to be reproduced

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  • Data are the facts you measure

  • They should be carefully recorded in an unbiased manner

  • They should be measured in a manner that minimizes random variation

  • They should be derived from the operational definitions you have developed

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Data Validation

  • Do the data make sense?

  • Look critically at the data

    • Highest and lowest values

    • Data entry errors

    • Distribution: Normal or skewed

  • Check selected data entries with original data forms

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Data Interpretation

  • Do not interpret/analyze data until after study is completed

  • Do not ‘unblind’ subjects until the study is completed other than for safety reasons

  • Do not interpret/analyze data until after data has been validated and the data set closed

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Data Interpretation

  • Use the research question and hypotheses to guide analyses

  • Use a priori definitions for any sub-set analyses

  • Exploration of epidemiologic data sets is OK, but need to avoid data mining

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Writing It Up

  • If you don’t write it, then it didn’t happen

  • Order of writing:

    • Methods

    • Results

    • Introduction

    • Discussion

    • Abstract

    • Title

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Writing It Up

  • After the first draft, new analyses will usually be suggested by the process of putting your ideas down on paper

  • Put the paper away for a few weeks and then read it again

  • Ask mentors and colleagues to read the paper at the first draft stage

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Sending It In

  • When writing the paper, have the journal you will submit to in mind

  • Pick journals that will match your paper’s topic and the quality and importance of your work

  • Aim high and, if needed, go low

  • Persist, Persist, Persist

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Clinical Research

Drug Development

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Drug Development

  • Preclinical/Laboratory Study

    • Cell culture in animal and human cells

    • Animal studies

    • Looking both at toxicity/carcinogenicity as well as effect, if relevant

  • Develop Investigational New Drug application with FDA (IND)

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Phase I Studies

  • Assess drug safety and tolerability

  • Healthy volunteers, then those with target disease

  • Pharmacokinetics

    • Absorption

    • Metabolism

    • Excretion

  • Dose escalation

  • 70% of new drugs pass this phase

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Phase II Studies

  • Assess drug efficacy

  • Usually randomized, controlled trials with smaller numbers up to several hundred subjects

  • Test different therapeutic strategies

  • Use surrogate variables and are usually short term

  • Only 1/3 get past phase II

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Phase III Studies

  • Large scale RCT to assess efficacy and safety of medication

  • Several hundred to thousands of patients enrolled

  • Classic randomized, placebo-controlled design

  • Long-term study design with real world outcome variables

  • Define package insert content and allow marketing

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Study Size and Adverse Events

  • The size of the treatment group determines the likely frequency of adverse events (side effects) that can be detected

  • A good rule of thumb is that you can detect an adverse event rate that is one event in the number of subjects divided by three:

    • A study with 100 patients will only detect AE’s that occur at a rate of 1/33 = 3%

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Phase IV Studies

  • Compare drugs with other drugs on the market

  • Define broader target population

  • Monitor long-term efficacy and safety

  • Conduct health economics assessment and quality of life study

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Reading Clinical Research

How to Approach RCT Reports

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Reading Clinical Trials

  • ‘All that glitters is not gold’ by Bengt and Curt Furberg

  • Just because a study is published in a journal does not mean that it represents truth

  • ‘Throwaways’ and Drug company sponsored newsletters have either no or limited peer review

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Was the question stated A Priori?

  • Exploring data is acceptable to define hypotheses, but cannot definitively answer them

  • Primary outcomes and limited secondary outcomes should be carefully defined before study commences

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Was the question stated A Priori?

  • Multiple hypothesis testing can lead to false association

  • P <0.05 is subverted if there are 20 looks at the data

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Is the question relevant?

  • Does the answer clarify whether the treatment will help patients to:

    • Feel better

    • Live longer

    • Have less complications of illness

  • Are the endpoints real world or merely surrogates

  • How can one generalize the findings?

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How is improvement quantified?

  • Are the outcomes relevant?

  • Do the measures used make sense?

  • Is the magnitude of the difference relevant to patient care?

  • Is the study ‘over-powered’?

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Are the outcomes relevant?

  • Quality of life

  • Mortality

  • Health economics

  • Surrogate markers of clinical outcome

  • Surrogate biologic markers

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How are adverse events measured?

  • Side effects are characterized as:

    • Severe: Treatment must be stopped, or patient hospitalized, or dies, or develops cancer, or has congenital anomaly in child

    • Moderate: Dosage must be reduced, usually leads to discomfort, temporary disability, or reduction in functioning

    • Mild: No change in treatment. Limited discomfort or dysfunction

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How are adverse events measured?

  • AE’s are characterized as to whether or not they are related to the medication:

    • Definitely

    • Likely

    • Probably

    • Possibly

    • Not associated

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Are the patients representative?

  • This is most problematic in pediatrics where we often have to extrapolate from adult studies

  • Gender, age, and race can all alter outcomes

  • Disease classification and severity can alter outcomes

  • High risk patients are usually excluded

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Where the groups initially comparable?

  • Even in studies of 150-200 subjects substantive imbalance can occur between treatment groups

  • Was stratification used to ensure balance?

  • Did the treatment group start out sicker so that they likely would improve more than the placebo group?

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Excluded Subjects?

  • Intent to treat analyses should be reported

  • Two unacceptable reasons to exclude subjects are:

    • After randomization where they do not meet entry criteria

    • Because they did not take the medication

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Do you need a statistician to read the study?

  • In clinical trials, design should allow relatively straightforward presentation of results

  • Effect size and relevance are more important than P values

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Do you need a statistician to read the study?

  • Consider the number of patients who would have to be treated to avoid the outcome being prevented

  • Subgroup analyses should be avoided unless defined a priori

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Economic Analysis

  • “Of course our drug is more expensive, but we need to convince clinicians to use it more”

  • Does the medication reduce direct or indirect costs or both?

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Economic Analysis

  • Be sensitive to relationship between the authors and the sponsor

  • Be careful if soft assumptions are used

  • Beware of analyses based on the clinical trial setting and not the real world

  • Beware indirect evidence with surrogate markers