06: Bayesian Factored Regression for Efficient Analysis of Two-Phase Study Designs

Thomas M. Menino Convention & Exhibition Center 

In research settings where collecting expensive or resource-intensive covariates from all subjects is impractical, two-phase designs can substantially improve study efficiency compared to simple random sampling by strategically selecting participants based on outcome data and available covariates. However, accounting for the missing at random covariates that were not sampled is a key analytical challenge in two-phase studies. Failure to properly address this missingness can introduce bias in parameter estimates. We propose flexible Bayesian factored regression using MCMC to model the distribution of the missing covariates jointly with the main analysis regression model of interest. Key advantages include applicability to both continuous and discrete covariates, extensibility to various covariate distributions, and stability in model fitting with small samples. We compare the performance of our Bayesian method to the previously studied Ascertainment-Corrected Maximum Likelihood (ACML) approach through extensive simulation studies. We also illustrate our approach with a case study using data from the Lung Health Study and introduce an R package implementing these methods.

Two-phase designs

Bayesian factored regression

Missing data

Longitudinal data 

Biometrics Section