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Foundations of Clinical Research: Minimizing Bias and Enhancing Causal Inference in Observational Clinical Research Al Mushlin, MD, ScM Department of Public Health Weill Medical College of Cornell University. October 7, 2013. How Research Works. ACTUAL STUDY. RESEARCH QUESTION. STUDY PLAN.

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Foundations of Clinical Research:Minimizing Bias and Enhancing Causal Inference in Observational Clinical Research Al Mushlin, MD, ScMDepartment of Public HealthWeill Medical College of Cornell University

October 7, 2013


How research works
How Research Works

ACTUAL STUDY

RESEARCH QUESTION

STUDY PLAN

design

implement

Target population

Intended

sample

Actual

subjects

Random & systematic error

Random & systematic error

Phenomena of interest

Intended

variables

Actual measurements

infer

infer

TRUTH IN THE

UNIVERSE

TRUTH IN THE

STUDY

FINDINGS IN

THE STUDY


Two kinds of errors no study is perfect
Two Kinds of Errors: no study is perfect!

errors

solutions

Study design, implementation, and quality control

Patient selection and sampling

Sample size, correct measurements and statistical calculations

  • Systematic (bias)

  • Random (statistical)


Bias

“Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease”

From Gordis, Epidemiology, 2000



Two general types of bias
Two General Types of Bias

  • Selection bias:

    The study does not reveal the truth because of the individuals studied.

  • Information (ascertainment/measurement) bias:

    The study does not reveal the truth because of errors in the information collected.


Types of selection bias
Types of Selection Bias

  • Problems with sampling, inclusion/exclusion criteria, or participation

  • The population(s) selected is not representative

    • Exclusion bias:

      • The study group excludes individuals who should have been in the population(s), limiting comparability.

    • Non-response bias:

      • People who do not participate are different from those who do.


Types of information bias
Types of Information Bias

  • Misclassification bias

  • Recall bias

  • Reporting bias

  • Detection bias

  • Measurement bias


Minimizing bias
Minimizing Bias

  • Use random or systematic sampling

  • Avoid unneeded inclusion and exclusion criteria

  • Employ accurate measurements

  • Use double-blinded procedures

    • Participants and investigators should be unaware of group assignments

  • Aim for high response rate


Systematic error and study validity
Systematic Error and Study Validity:

  • How well study measured what it intended to measure (internal validity)

  • How well the study measured truth in the universe (external validity)

  • Internal validity is necessary but not sufficient for external validity

  • internal plus external validity are required for "generalizability”


The next important question
The Next Important Question:

  • Even if there is no bias or random error in the study, what are explanations for the results other than what was found is a true association; that it is a cause-and-effect relationship?


The next important question1
The Next Important Question:

  • Even if there is no bias or random error in the study, what are explanations for the results other than what was found is a true association; that it is a cause-and-effect relationship?

  • Answer:

    • The relationship is reversed: effect-cause

    • Another factor/variable is confounding the relationship…….and is the real cause


Confounding

Exposure

Confounder

Outcome

Confounding

An apparent relationship between an exposure and an outcome that is really caused by a 3rd variable (the confounder)


Definition of confounding
Definition of Confounding

  • When there is a variable that is associated both with the predictor (independent) variable and a cause of the outcome (dependent) variable


Coffee and risk of mi
Coffee and Risk of MI

  • Research question: Is coffee drinking a cause of myocardial infarction?

  • Design: Case-control study

  • Results:

OR=2.25

From Designing Clinical Research, 2013: Appendix 9A


Possible interpretations
Possible Interpretations:

  • Study has revealed the truth

  • Study finding is due to chance

  • Study is biased

  • Study findings are confounded by another unexamined variable


Is there a confounder
Is There a Confounder?

Coffee Drinking

?

Myocardial Infarction


Example coffee and smoking
Example : Coffee and Smoking

OR = 16

Coffee drinking is associated with smoking

From DCR: Appendix 9A


Is there a confounder1
Is There a Confounder?

Coffee

Smoking

MI


Smoking and mi
Smoking and MI

OR = 4

Smoking is associated with MI

From DCR Appendix 9A


Smoking is a potential confounder
Smoking is a Potential Confounder

Coffee Drinking

Smoking

MI


A stratified analysis confirms a confounder
A Stratified Analysis Confirms a Confounder

Smokers

Non Smokers

OR=1

OR=1

From DCR: Appendix 9A


A stratified analysis confirms a confounder1
A Stratified Analysis Confirms a Confounder

OR=2.25

OR = 1

From DCR: Appendix 9A


Minimizing confounding
Minimizing Confounding

  • Measure potential confounders!

  • Consider options during the design or latter, during the analysis

    • Cannot adjust for unconsidered or unmeasured potential confounders

  • Randomize when feasible and ethical


Study design options to control for confounders
Study Design Options to “Control” for Confounders

  • Sampling

    • Specification

    • Matching/stratification

  • “Opportunistic” Study Designs

    • Natural experiments

    • Mendelian randomization

    • Instrumental variables


Analyses adjusting for confounders
Analyses Adjusting for Confounders

  • Stratification

  • Multivariate Adjustment

    • Creates a hypothetical estimate (a model) of risk

      • What would the odds of MI be in coffee drinkers vs. non drinkers if smoking (and other potential confounders) were equally frequent in both groups?

  • Propensity Scores

    • When “confounding by indication” is a concern


Interaction and effect modifiers

Exposure

Effect Modifier

Outcome

“Interaction” and Effect Modifiers

SUBGROUP 1

SUBGROUP 2

Exposure

Effect Modifier

Outcome


A stratified analysis suggests that coffee is an effect modifier
A Stratified Analysis Suggests that Coffee is an Effect Modifier

OR = 11

OR = 1.01

Source: DCR Appendix 9A


The challenge is to identify variables in the causal pathway
The Challenge is to Identify Variables in the Causal Pathway Modifier

Exposure

Other Variables in the Causal Pathway

Outcome


Additional evidence for causality
Additional Evidence for Causality Modifier

  • Consistency of finding

  • Strength of the association

  • Dose response relationship

  • Biologic plausibility


Take home messages
Take Home Messages Modifier

  • Be aware of threats to the validity of your study: chance, bias, directionality, confounding

  • Anticipate and address problems before you begin

  • Recognize the need to tease apart confounders and effect modifiers

  • Consider the strength of the evidence for causality


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