Two-phase designs with current status data

We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample. The design challenge is then to select the phase II sub-sample in order to maximize the precision of the marker effect on the time of interest under a proportional hazards model. This problem has not been examined before for current status data and the role of the assessment time is highlighted. Inference based on likelihood and inverse probability weighted estimating functions are considered, with designs centered on score-based residuals, extreme current status observations, or stratified sampling schemes. Data from a registry of patients with psoriatic arthritis is used in an illustration where we study the risk of diabetes as a comorbidity.

Automated summary

In this study, we focus on the design and analysis of two-phase studies to assess the relationship between a fixed marker (e.g., genetic) and event time under current status observation. We propose a design challenge of selecting a phase II sub-sample to maximize the precision of the marker effect, considering likelihood and weighted estimating functions for inference. Different design strategies are explored using registry data of patients with psoriatic arthritis to study the risk of diabetes as a comorbidity.