Yei Eun Shin

Pooling controls from nested case–control studies with the proportional risks model

Abstract The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case–control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case–control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study.

Dynamic prediction by landmarking with data from cohort subsampling designs

Longitudinal data are often available in cohort studies and clinical settings, such as covariates collected at cohort follow-up visits or prescriptions captured in electronic health records. Such longitudinal information, if correlates with the health event of interest, may be incorporated to dynamically predict the probability of a health event with better precision. Landmarking is a popular approach to dynamic prediction. There are well-established methods for landmarking using full cohort data, but collecting data on all cohort members may not be feasible when resource is limited. Instead, one may select a subset of the cohort using subsampling designs, and only collect data on this subset. In this work, we present conditional likelihood and inverse-probability weighted methods for landmarking using data from cohort subsampling designs, and discuss considerations for choosing a particular method. Simulations are conducted to evaluate the applicability of the methods and their predictive performance in different scenarios. Results show that our methods have similar predictive performance to the full cohort analysis but only use small fractions of the full cohort data. We use real nested case-control data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial to illustrate the methods.