We developed a simulation tool to explore tradeoffs in statistical efficiency when using different matching criteria to create a nested case-control study from a larger cohort.
For multivariable analyses of cancer outcomes in cohort studies, Cox Proportional Hazard models are commonly used and the resulting Hazard Ratio is often interpreted as an estimate of the incidence rate ratio (IRR).
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Thus all nested case-control studies match on duration of follow-up.
Further matching of controls to cases can be implemented so that controls and cases are similar on covariate characteristics.
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When paired with the appropriate analytic methods, a nested case-control study, which uses all or a subset of cases along with matched controls, estimates the rate ratio that would otherwise have been observed in a full cohort analysis.
Since the nested case-control design requires the collection and measurement of exposure, covariate, and biomarker data on fewer subjects than a full cohort analysis would, the design is logistically efficient.