AbstractThe determination of risk factors for disease incidence has been the subject of much epidemiologic research. With this goal a common study design entails the follow‐up of an initially disease‐free cohort, keeping track of the dates of disease incidence (onset) and ascertaining covariate (putative risk factor) information on the full cohort. However, the collection of certain covariate information on all study subjects may be prohibitively expensive and, therefore, the nested case‐control study has commonly been used. The high cost of full covariate information on all subjects also arises when determining risk factors for “failure,” death say, “following” disease onset, in particular, in a prevalent cohort study with follow‐up; in such a study a cohort of subjects with existing disease is followed. We here adapt nested case‐control designs to the setting of a prevalent cohort study with follow‐up, a topic previously not addressed in the literature. We provide the partial likelihood under risk set sampling and state the asymptotic properties of the estimated covariate effects and baseline cumulative hazard. We address the following design questions in the context of prevalent cohort studies with follow‐up: How many subjects should be included in the sampled risk sets for efficient estimation? In what way is the proportion of censored subjects associated with the benefit of a nested case‐control design? What proportion of overall variance is attributable to risk set sampling? This work is motivated by the anticipated analysis of data on survival with Parkinson's Disease, being collected as part of the ongoing Canadian Longitudinal Study on Aging. The Canadian Journal of Statistics 45: 4–28; 2017 © 2017 Statistical Society of Canada