Correcting Bias from Covariate-Dependent Censoring in Survival Disparity Decomposition

Thomas M. Menino Convention & Exhibition Center 

Research on racial and socioeconomic health disparities (HD) in time-to-event outcomes is crucial to
public health. Yet, existing HD decomposition methods can yield biased estimates when censoring, such as dropout or
competing risks, depends on covariates, including demographic, socioeconomic, or behavioral characteristics. Widely
applied Peters–Belson (PB) approaches to time-to-event outcomes use Kaplan–Meier (KM) estimation to quantify
overall HD and Cox regression to predict counterfactual survival. The resulting HD decomposition can be biased:
KM assumes covariate-independent censoring, whereas Cox regression assumes independent censoring conditional on
modeled covariates. To address this limitation, we propose a novel decomposition method that integrates propensity
score (PS) methodology with inverse probability of censoring weights (IPCW). The PS component balances covariates
across groups to estimate counterfactual survival without specifying an outcome model, while the IPCW component
corrects for covariate-dependent censoring by upweighting uncensored individuals to represent those who were censored.
Developed within a semiparametric framework, the proposed IPCW-PS approach reduces bias from traditional
KM-Cox methods. Simulation studies demonstrate substantial bias reduction under dependent censoring. We further
apply IPCW-PS to NielsenIQ Consumer Panel data to examine smoking cessation disparities between White and
Black households and find that smoke-free laws contribute meaningfully to explaining observed disparities in smoking
cessation patterns.

Survival analysis

health disparity

Peters–Belson

inverse probability of censoring weights

propensity score