A calibrated Bayesian method for the stratified proportional hazards model with missing covariates.

ABSTRACT: Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion. The inverse probability weighting method is widely used to handle missing covariates. However, obtaining asymptotic variance in frequentist inference is complicated because it involves estimating parameters for propensity scores. In this paper, we propose a new approach based on an approximate Bayesian method without using Taylor expansion to handle missing covariates for survival data. We consider a stratified proportional hazards model so that it can be used for the non-proportional hazards structure. Two cases for missing pattern are studied: a single missing pattern and multiple

SUBMITTER: Kim S 

PROVIDER: S-EPMC8977246 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature