confounding and bias...confounding and bias cdr frank williams united states navy navy medical...
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![Page 1: Confounding and Bias...Confounding and Bias CDR Frank Williams United States Navy Navy Medical Research Unit #3 - Cairo CSTC-A Command Surgeon Office Bias and Confounding •Key difference](https://reader035.vdocument.in/reader035/viewer/2022062602/5edf75cdad6a402d666aceb1/html5/thumbnails/1.jpg)
Confounding and Bias
CDR Frank Williams
United States Navy
Navy Medical Research Unit #3 - Cairo
CSTC-A Command Surgeon Office
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Learning Objectives
• Understand the concepts of confounding and
bias
• Understand how confounding and bias can be
controlled
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Bias and Confounding
• Key difference between bias and confounding
– Bias is a systematic characteristic introduced by the
investigator
– Confounding is a relationship of some characteristic
that is associated with the exposure and
independently a risk factor for disease and is not on
the causal pathway
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Types of Bias
• Two broad categories of bias
– Selection Bias
– Observation (information) bias
• Under each general term specific types of bias
occur
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Selection Bias
• Can result from the way that individuals are
selected for the study
– Differential surveillance, diagnosis or referral
• Classic example is thromboembolism and oral
contraceptive use
• Refusal or non-response among either study
group
– If rates of response are related to exposure status
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Observation or Information Bias
• Systematic differences in the way data on exposure or outcome are obtained from the various study groups
– Inaccurate or Incomplete
• If it affects the groups to different degrees
• Recall Bias
• Interviewer Bias
• Lost to follow up
• Misclassification
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Control of Bias
• Choice of Study Population
– Using hospital controls in case-control studies
• Decreases likelihood of non-response, selection, and recall
bias
• Use well defined population relative to some
defined characteristic (occupation, area of
residence, alumni, etc) that gains access to
centralized data
– Decreases the likelihood of loss to follow up
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Control of Bias
• Methods of data collection
– Use specific instruments, such as validated questionnaires, standard physical exam forms, data extracted from a record
– Administer the instruments by study personnel only
– Example: if you are studying hypertension, collect data from several blood pressure readings given by a trained study person rather than asking a participant about hypertensive history
– Try to maintain blindness
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Bias – One Final Thought
• All analytical studies are susceptible to bias, but each study may be more susceptible to a particular bias
– Case – control
• Disease status may influence determination of exposure status- recall bias
• Exposure status may influence identification of disease and non- disease participants- selection bias
– Cohort studies
• Loss of follow-up, (retrospective) selection bias
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CONFOUNDING FACTORS• Confounders are related to both the outcome of
interest and exposure not on the causal pathway
Exposure
Confounding Factor
Outcome
Confounding FactorExposure Outcome
NOT CONFOUNDING
CONFOUNDING
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Confounders
• Confounding can be either positive or negative in terms of the confounders effect on the outcome of interest.
• Positive confounding results in an over-estimation of the association between exposure and outcome
• Negative confounding results in an under-estimation of the association between exposure and outcome
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Controlling Confounding in the
Design
• Randomization of the study participants is the preferred method in interventional studies
– This will distribute a potential confounder (either known or unknown) evenly over the treatment groups, but the sample size must be sufficiently large
• Restriction can be used if the potential confounder does not vary across either the exposure or disease
– If sex and race are confounders then restrict the participants to nonwhite men or white women
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Controlling Confounding in the
Design
• Matching
– If matching is used, then this must be considered in both the
design phase and the analysis phase of the study
– An example of matching would be:
• Lets say age, sex and smoking are potential confounders for AMI, a
65 year old woman who is an MI patient would be matched with a 65
year old women with the same smoking history who never had an MI
– Matching is used primarily in case-control studies and in the
analysis phase you must stratify the analysis based upon the
matching factors
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Controlling Confounding in the
Analysis
• Stratified analysis can be used in any study, however,
must be used in a matched case-control study
– Rule to live by= if you stratify the design, then stratify the
analysis
• Multivariate analysis
– If there are several potential confounders, stratification will
not be adequate
– Multivariate analysis are commonly used and in general
multiple regression analysis is the most common.
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Questions???