Bayesian subcohort selection for longitudinal covariate measurements in follow-up studies

TL;DR

This paper proposes a Bayesian optimal design method for selecting subcohorts in longitudinal studies with multiple covariates and measurement points, improving the precision of covariate effect estimates under budget constraints.

Contribution

It extends previous optimal subcohort selection methods to multiple covariates and time points using a Bayesian sequential design approach.

Findings

Bayesian optimal design improves estimate precision.

Sequential design outperforms simple random sampling.

Method tested with simulated and real data.

Abstract

We consider planning longitudinal covariate measurements in follow-up studies where covariates are time-varying. We assume that the entire cohort cannot be selected for longitudinal measurements due to financial limitations and study how a subset of the cohort should be selected optimally in order to obtain precise estimates of covariate effects in a survival model. In our approach, the study will be designed sequentially utilizing the data collected in previous measurements of the individuals as prior information. We propose using a Bayesian optimality criterion in the subcohort selections, which is compared with simple random sampling using simulated and real follow-up data. This study extends previous results where optimal subcohort selection was studied with only one re-measurement and one covariate, to more realistic cases where several covariates and measurement points are allowed. Our results support the conclusion that the precision of the estimates can be clearly improved by optimal design.