Bias confounding and the role of chance
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Bias, Confounding and the Role of Chance. Principles of Epidemiology Lecture 5 Dona Schneider, PhD, MPH, FACE. 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

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Bias, Confounding and the Role of Chance

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Bias confounding and the role of chance

Bias, Confounding and the Role of Chance

Principles of Epidemiology

Lecture 5

Dona Schneider, PhD, MPH, FACE


To show cause we use

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

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 confounding and the role of chance

BIAS

Systematic error built into the study design

Selection Bias

Information Bias


Types of selection 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

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

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

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

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 cont1

Information Bias (cont.)

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


Types of misclassification bias

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

True Classification

Misclassification Bias (cont.)

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

Differential misclassification - Overestimate exposure for 10 cases, inflate rates

Cases

Controls

Total

Exposed

110

50

160

Nonexposed

40

50

90

150

100

250

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


Misclassification bias cont1

True Classification

Misclassification Bias (cont.)

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

Differential misclassification - Underestimate exposure for 10 cases, deflate rates

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


Misclassification bias cont2

True Classification

Misclassification Bias (cont.)

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

Differential misclassification - Underestimate exposure for 10 controls, inflate rates

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


Misclassification bias cont3

True Classification

Misclassification Bias (cont.)

Cases

Controls

Total

Exposed

100

50

150

50

50

100

Nonexposed

150

250

100

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

Differential misclassification - Overestimate exposure for 10 controls, deflate rates

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


Misclassification bias cont4

Misclassification Bias (cont.)

  • Nondifferential (random) misclassification – errors in assignment of group happens in more than one direction

    • This will dilute the study findings - BIAS TOWARD THE NULL


Misclassification bias cont5

True Classification

Misclassification Bias (cont.)

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

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

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


Controls for bias

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


Controls for bias cont

Controls for Bias (cont)

  • 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


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