This benefit is pronounced when the covariate of interest is biological, since assessments such as gene expression profiling are expensive, and because the quantity of blood available for such analysis is often limited, making it a valuable resource that should not be used unnecessarily.
[2][4] The analysis of a nested case–control model must take into account the way in which controls are sampled from the cohort.
Failing to do so, such as by treating the cases and selected controls as the original cohort and performing a logistic regression, which is common, can result in biased estimates whose null distribution is different from what is assumed.
Ways to account for the random sampling include conditional logistic regression,[5] and using inverse probability weighting to adjust for missing covariates among those who are not selected into the study.
All cases who developed the outcome of interest during the follow-up are selected and compared with a random sample of the cohort.