8361.ppt

Bias, Confounding and

the Role of Chance

Principles of Epidemiology

Lecture 5

Dona Schneider, PhD, MPH, FACE

Epidemiology (Schneider)

To Show Cause We Use Koch’s Postulates for Infectious Disease Hill’s Postulates for Chronic Disease and Complex Questions

Strength of Association – Tonight’s entire lecture Biologic Credibility Specificity Consistency with Other Associations Time Sequence Dose-Response Relationship Analogy Experiment Coherence


To Show a Valid Statistical Association We need to assess:

Bias: whether systematic error has been built into the study design

Confounding: whether an extraneous factor is related to both the disease and the exposure

Role of chance: how likely is it that what we found is a true finding

BIAS

Systematic error built into the study design

Selection Bias

Information Bias


Types of Selection Bias

Berksonian bias – There may be a spurious

association between diseases or between a characteristic and a disease because of the different probabilities of admission to a hospital for those with the disease, without the disease and with the characteristic of interest Berkson J. Limitations of the application of fourfold table

analysis to hospital data. Biometrics 1946;2:47-53


Types of Selection Bias (cont.)

Response Bias – those who agree to be in

a study may be in some way different from

those who refuse to participate

Volunteers may be different from those who

are enlisted


Types of Information Bias

Interviewer Bias – an interviewer’s

knowledge may influence the structure of

questions and the manner of presentation,

which may influence responses

Recall Bias – those with a particular outcome

or exposure may remember events more clearly

or amplify their recollections


Types of Information Bias (cont.)

Observer Bias – observers may have

preconceived expectations of what they

should find in an examination

Loss to follow-up – those that are lost to

follow-up or who withdraw from the study

may be different from those who are followed

for the entire study


Information Bias (cont.) Hawthorne effect – an effect first documented

at a Hawthorne manufacturing plant; people act

differently if they know they are being watched

Surveillance bias – the group with the known

exposure or outcome may be followed more

closely or longer than the comparison group


Information Bias (cont.)

Misclassification bias – errors are made in classifying either disease or exposure status


Types of Misclassification Bias

Differential misclassification – Errors in

measurement are one way only

Example: Measurement bias –

instrumentation may be inaccurate, such as

using only one size blood pressure cuff to

take measurements on both adults and

children

Misclassification Bias (cont.)

250100150

1005050Nonexposed15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

250100150

905040Nonexposed

16050110ExposedTotalControlsCases

OR = ad/bc = 2.8; RR = a/(a+b)/c/(c+d) = 1.6

Differential misclassification - Overestimate exposure for 10 cases, inflate rates


Cases Controls Total

Exposed 100 50 150

Nonexposed 50 50 100

150 100 250

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification


Exposed 90 50 140


150 100 250

OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

Differential misclassification - Underestimate exposure for 10 cases, deflate rates



Exposed 100 50 150


150 100 250

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification


Exposed 100 40 140


150 100 250

OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

Differential misclassification - Underestimate exposure for 10 controls, inflate rates


2501001501005050Nonexposed15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification


Exposed 100 60 160

Nonexposed 50 40 90

150 100 250

OR = ad/bc = 1.3; RR = a/(a+b)/c/(c+d) = 1.1

Differential misclassification - Overestimate exposure for 10 controls, deflate rates



Nondifferential (random)

misclassification – errors in assignment

of group happens in more than one

direction

This will dilute the study findings -

BIAS TOWARD THE NULL



Exposed 100 50 150


150 100 250

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification


Exposed 110 60 170

Nonexposed 40 40 80

150 100 250OR = ad/bc = 1.8; RR = a/(a+b)/c/(c+d) = 1.3

Nondifferential misclassification - Overestimate exposure in 10 cases, 10 controls – bias towards null

Controls for Bias Be purposeful in the study design to minimize the chance for

bias Example: use more than one control group

Define, a priori, who is a case or what constitutes exposure so that there is no overlap Define categories within groups clearly (age groups, aggregates of

person years)

Set up strict guidelines for data collection Train observers or interviewers to obtain data in the same fashion It is preferable to use more than one observer or interviewer, but not

so many that they cannot be trained in an identical manner

Randomly allocate observers/interviewer data collection assignments

Institute a masking process if appropriate Single masked study – subjects are unaware of whether they are in

the experimental or control group

Double masked study – the subject and the observer are unaware of the subject’s group allocation

Triple masked study – the subject, observer and data analyst are unaware of the subject’s group allocation

Build in methods to minimize loss to follow-up

Controls for Bias (cont)

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8361.ppt