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A short introduction to epidemiology Chapter 10: Interpretation

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Title: A short introduction to epidemiology Chapter 10: Interpretation


1
A short introduction to epidemiologyChapter 10
Interpretation
  • Neil Pearce
  • Centre for Public Health Research
  • Massey University, Wellington,
  • New Zealand

2
Chapter 10Interpretation
  • Appraisal of a single study
  • Appraisal of all of the available evidence

3
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

4
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

5
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?

6
Cohort Studies of Shipyard Welding and Lung Cancer
7
Appraisal of a Single StudySystematic Error
  • What are the likely strengths and directions of
    possible biases?

8
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

9
Healthy Worker Effect in a Longitudinal Study of
FEV1 and Exposure to Granite Dust
10
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

11
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

12
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

13
Assessment of Possible Confounding by Smoking in
a Study of Lung Cancer and Occupation
14
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

15
Chapter 10Interpretation
  • Appraisal of a single study
  • Appraisal of all of the available evidence

16
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

17
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

18
Cohort Studies of Shipyard Welding and Lung Cancer
19
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

20
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

21
Case-Control Studies of Phenoxy Herbicides and STS
22
Case-Control Studies of Phenoxy Herbicides and NHL
23
New Zealand Case-Control Study of Phenoxy
Herbicides and NHL
24
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

25
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

26
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

27
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

28
A short introduction to epidemiologyChapter 10
Interpretation
  • Neil Pearce
  • Centre for Public Health Research
  • Massey University, Wellington,
  • New Zealand
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