Cumulative risk regression in case–cohort studies using pseudo-observations


Case–cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case–cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case–cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case–cohort survival data. We adjust for the case–cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

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We are grateful for constructive comments from the referees and an associate editor. The data from Danish Diet, Cancer, and Health Cohort was kindly made available by Lotte Maxild Mortensen. The work presented in this article is supported by Novo Nordisk Foundation grant NNF17OC0028276.

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Correspondence to Erik T. Parner.

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Parner, E.T., Andersen, P.K. & Overgaard, M. Cumulative risk regression in case–cohort studies using pseudo-observations. Lifetime Data Anal (2020).

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  • Case–cohort study
  • Competing risks
  • Cumulative incidence
  • Cumulative risk
  • Pseudo-observations