A short introduction to epidemiology Chapter 10: Interpretation

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A short introduction to epidemiologyChapter 10
Interpretation

• Neil Pearce
• Centre for Public Health Research
• Massey University, Wellington,
• New Zealand

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Chapter 10Interpretation

• Appraisal of a single study
• Appraisal of all of the available evidence

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Interpretation of EvidenceFrom Epidemiological
Studies

• Populations do not randomize themselves by
  exposure status
• They do not always respond to requests to
  participate in epidemiological studies
• They may supply incomplete exposure information
• They cannot be asked about unknown risk factors
• It is not possible to do perfect studies, and we
  have to make decisions based on imperfect
  information

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Summary of Study Design Issues

• Reduce random error by making the study as large
  as possible and through appropriate study design
• Minimize selection bias by having a good response
  rate (and selecting controls appropriately in a
  case-control study)
• Ensure that information bias is non-differential
  and keep it as small as possible
• Minimize confounding in the study design and
  control for it in the analysis

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Appraisal of a Single Study Random Error

• What is the magnitude and precision of the effect
  estimate?
• Are the study findings consistent with those of
  previous studies?

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Cohort Studies of Shipyard Welding and Lung Cancer
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Appraisal of a Single StudySystematic Error

• What are the likely strengths and directions of
  possible biases?

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Selection Bias

• Selection bias is any bias arising from the way
  that study participants are selected (or select
  themselves) from the source population
• If selection bias cannot be avoided or
  controlled, then it may still be possible to
  assess its likely strength and direction

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Healthy Worker Effect in a Longitudinal Study of
FEV1 and Exposure to Granite Dust
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Information Bias

• May occur when there is misclassification of
  exposure or disease
• If misclassification of exposure (or disease) is
  unrelated to disease (or exposure) then the
  misclassification is non-differential
• If misclassification of exposure (or disease) is
  related to disease (or exposure) then the
  misclassification is differential

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Information Bias

• Is information bias likely to be differential or
  non-differential?
• If it is non-differential, then a positive
  findings unlikely to be explained by
  misclassification, but a negative finding may be
  a false negative

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Confounding

• Occurs when the exposed and non-exposed groups in
  the source population are not comparable, because
  of inherent differences in background disease
  risk
• If there is the potential for uncontrolled
  confounding, then it is important to attempt to
  assess its likely strength and direction

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Assessment of Possible Confounding by Smoking in
a Study of Lung Cancer and Occupation
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Appraisal of a Single Study

• The two most common criticisms of epidemiological
  studies are the possibility of uncontrolled
  confounding misclassification of exposure or
  disease (information bias)
• Uncontrolled confounding is often weaker than
  might be expected
• Non-differential information bias will usually
  produce false negative findings

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Chapter 10Interpretation

• Appraisal of a single study
• Appraisal of all of the available evidence

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Appraisal of All of the Available Evidence
Criteria for Assessing Causality (Bradford-Hill)

• Criteria based on epidemiological evidence
• Temporality
• Specificity
• Consistency
• Strength of association
• Dose-response

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Meta-Analysis Benefits

• Meta-analysis may reduce the possibility of false
  negative results because of small numbers in
  specific studies
• It may enable the effect of exposure to be
  estimated with greater precision

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Cohort Studies of Shipyard Welding and Lung Cancer
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Meta-Analysis Limitations

• Strikingly different results can be obtained
  depending on which studies are selected
• Meta-analysis reduces random error but does not
  necessarily reduce systematic error, and may even
  increase it
• Meta-analysis therefore involves the same issues
  as in a report on a single study, and both
  quantitative and narrative elements are required

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Meta-Analysis Assessment of Possible Biases

• An advantage of meta -analyses is that possible
  biases can be addressed using actual data rather
  than hypothetical examples
• For example, if smoking information is not
  available in all studies, the extent of
  confounding by smoking can be assessed in those
  studies in which smoking information is available
• Similarly, the possibility of information bias
  can be assessed by contrasting particular studies

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Case-Control Studies of Phenoxy Herbicides and STS
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Case-Control Studies of Phenoxy Herbicides and NHL
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New Zealand Case-Control Study of Phenoxy
Herbicides and NHL
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Appraisal of All of the Available Evidence
Criteria for Assessing Causality (Bradford-Hill)

• Criteria based on comparing epidemiological
  evidence with evidence from other sources
• Plausibility
• Coherence

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Biological Plausibility

• Many major epidemiological findings (e.g. on
  occupational carcinogens) were not biologically
  plausible at the time they were first discovered
• In many instances it has taken many years in the
  laboratory to ascertain the mechanism involved in
  established epidemiological findings
• Biological implausibility should not, by itself,
  be used to dismiss epidemiological findings

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Interpretation of EvidenceFrom Epidemiological
Studies

• The most common criticisms of epidemiological
  findings are
• There may be uncontrolled confounding
• Information on exposure and/or disease is not
  perfect
• The findings lack biological plausibility

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Interpretation of EvidenceFrom Epidemiological
Studies

• None of these considerations are sufficient in
  themselves to dismiss the findings of an
  epidemiological study
• Assessment of epidemiological findings should be
  based on all of the available evidence
• It is important to assess the likely strength and
  direction of possible biases

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A short introduction to epidemiologyChapter 10
Interpretation

• Neil Pearce
• Centre for Public Health Research
• Massey University, Wellington,
• New Zealand