Design of large case

W. Dana Flanders
Emory University
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Study Goal
Cohort Studies
Cases and Controls
Validity Issues
Spatial comparison
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Before studying something we ought to
define what we wish to study.
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Obvious. A bit like some Yogisms
“It ain’t over til it’s over.”
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Study Goal
◦ Presumed; explicit
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Estimate Causal Risk Ratio
◦ Risk in exposed group divided by what the risk in
the exposed group would have been, had they been
unexposed
◦ Counterfactual
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Causal Risk Ratio ≈ Causal Rate Ratio
◦ rare disease (e.g. Cancer for short periods)
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Occasionally in case-control studies
(population known), Causal Risk Difference,
others
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To design a case-control study, first
conceptually design a cohort study
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To design a case-control study, first
conceptually design a cohort study
Why?
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To design a case-control study, first
conceptually design a cohort study
Because, then, one can argue rigorously that
the odds ratio has meaning in terms of risk
ratios, or other meaningful measures (e.g.
rate ratio)
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To design a case-control study, first
conceptually design a cohort study
◦ Population (inclusion, exclusion criteria)
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To design a case-control study, first
conceptually design a cohort study
◦ Population (inclusion, exclusion criteria)
◦ Exposure, covariates (source, collection)
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To design a case-control study, first
conceptually design a cohort study
◦ Population (inclusion, exclusion criteria)
◦ Exposure, covariates (source, collection)
◦ Follow-up (identification of incident cases)
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To design a case-control study, first
conceptually design a cohort study
◦ Population (inclusion, exclusion criteria)
x What is the underlying source population for cases
◦ Exposure, covariates (source, collection)
x Which variables, how to collect
◦ Follow-up (identification of incident cases)
x Means of identifying cases during observation period,
and link to underlying source population
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To design a case-control study, first
conceptually design a cohort study
From another viewpoint, to identify casesone must observe, sometimes incompletely,
some cohort (the source population)
Recruiting
Controls
Identifying
Cases
Source
population
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Ideally, will identify and include all, or nearly
all, cases in a defined cohort of interest (just
like in a cohort study)
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Ideally, will identify and include all, or nearly
all, cases in a defined cohort of interest (just
as in a cohort study)
Controls are then a random sample from this
source population
Information on exposures and confounders is
available in both cases and controls
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Design for selecting controls is fairly
straightforward, once know the cohort
Some options and general principles next
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General Principles
◦ Purpose of controls – provide estimate of exposure
frequency in source population
◦ good control group: random sample from
underlying source, perhaps some special eligibility
criteria
◦ corollary - selection is independent of exposure
(conditional on stratification, if any)
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General Principles
◦ Some specific types, and key advantage
x Risk set sampling: sample from source population,
just when each case develops – allows estimation of
rate ratio, no rare disease (person-time)
x Density-sampling – sample controls to estimate p-t,
e.g. dynamic cohort
x Sampling from the source population at start of
follow-up - allows estimation of risk ratio, no rare
disease (cohort)
x Matching – allows efficient control of confounding by
the matching factors
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General Principles
◦ Some common sources
x RDD – population based, but account for cell phones
x Neighborhood – often similar SES, so control efficient,
but probably out here
x Hospital- very difficult
x Insurance, other rosters
◦ But whatever it is: a good rule is try to have the
exposure in controls be representative of that in the
source population
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Key threats are the usual selection bias,
confounding, misclassification
Apply to both cohort and case-control
Special care in case-control study to
◦ Assure temporality (exposure precedes disease)
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Case-control studies often more subject to
selection bias
◦ Cases and controls may not be from same cohort
◦ Participation occurs after disease, and exposure
known: can depend on both leading to bias
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Observational Study of radiation exposure
◦ Exposure varies with space and time, so primarily
spatial, perhaps temporal-spatial comparisons
x Risk in one area (exposure level) compared with that in
another area (different exposure)
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Opens door for confounding by risk factors
that vary spatially, or in time and space
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SES
Lifestyle, smoking, alcohol, nutrition, diet
Genetics (neighborhoods?)
Occupation
Access to care
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Observational Study of radiation exposure
◦ Exposure varies with space and time, so primarily
spatial, perhaps temporal-spatial comparisons
x Risk in one area (exposure level) compared with that in
another area (different exposure)
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Opens door for confounding by risk factors that
vary spatially, or in time and space
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SES
Lifestyle, smoking, alcohol, nutrition, diet
Genetics (neighborhoods?)
Occupation
Access to care
Issues the same in Cohort and Case-control
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Think cohort first, then case-control
Case-control similar to cohort, but with
sampling to save time and money
Case-control can yield valid estimates: Rate
or Risk Ratios, sometimes even differences
Extra challenges – participation, assuring
cases and controls come from same cohort
Common challenge to both- confounding
“It ain’t over til its over.”
The first part of this presentation is over, but
time remains for questions.
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Assumptions
◦ The distribution of residences with exposures X and
specular exposure Y is same as distribution of
residences with exposures Y and specular exposure
X (symmetry of actual and specular exposure
distributions)
◦ The actual and specular residences are similar w/r/t
confounders