Nested Case-Control Study

A traditional case-control study recruits cases from a hospital or registry and selects controls from the general population, making it difficult to ensure that controls came from the same source population as cases. A nested case-control study draws both cases and controls from a defined cohort with known entry and follow-up dates, so the source population is explicit and controls are guaranteed to have been at risk of becoming cases — this removes a major source of selection bias and allows the OR to estimate the IRR.

Selecting 4–5 controls per case captures most of the statistical efficiency gain relative to the full cohort analysis; beyond a ratio of about 10:1 the marginal gain is negligible. The optimal number depends on the relative cost of obtaining case versus control data and on statistical power requirements. For rare outcomes where each case is precious, up to 10 controls per case may be justified.

Both designs sample from a cohort to reduce exposure-ascertainment costs. In a nested case-control study, controls are risk-set matched to each case at the time of the case event, and conditional logistic regression is used. In a case-cohort study, a random sub-cohort is selected at baseline and serves as the comparison group for all cases regardless of when they occur; this allows the same sub-cohort to be used for multiple outcomes but requires more complex weighted analyses. Nested case-control designs are generally simpler to analyse; case-cohort designs are preferred when multiple outcomes will be studied simultaneously.

Yes. Although the design is conceptually prospective — exposure precedes outcome — it can be embedded in a retrospective cohort assembled from historical records. The key requirements are that the cohort's entry dates, follow-up periods, and outcome dates are reliably documented, and that exposure information can be retrieved for the selected cases and controls without knowledge of case status (to avoid information bias).

Conditional logistic regression for matched case-control data is available in R (clogit function in the survival package), Stata (clogit command), SAS (PROC LOGISTIC with STRATA statement), and SPSS. The matching variable (usually a case-set ID) must be included as the stratum identifier. For time-to-event analyses using the full risk-set, the Cox partial likelihood with time-matching is mathematically equivalent to conditional logistic regression under incidence density sampling.