Quantitative Issues in Cancer Research Working Group Seminar

Amy Zhou, PhD Student, Department of Biostatistics, Harvard University

Comparison of Outcome-Dependent Sampling for Semi-Competing Risks

Abstract: Outcome-dependent sampling is a commonly used design tool to collect otherwise unavailable information on a subset of participants rather than all participants. This is particularly useful in research settings where one or more covariates of interest may not be readily available, whether cost-prohibitive, time-consuming, or difficult to obtain in a resource-limited setting. Two common outcome-dependent sampling methods used in time-to-event settings are nested case-control and case-cohort. Classes of designs for both nested case-control and case-cohort were developed to extend their use to analysis of semi-competing risks. Semi-competing risks refers to the setting where interest lies in some non-terminal event, the occurrence of which is subject to some terminal event (typically, but not always, death). We compare the efficiency of these two designs for semi-competing risks through simulation to show the effect of censoring, type of risk factor, subcohort size, and more and illustrate the flexibility of these two designs to tailor resource allocation that best suit the disease context and study goals.