  Restricted mean survival time (RMST) is an intuitive summary statistic for
time-to-event random variables, and can be used for measuring treatment
effects. Compared to hazard ratio, its estimation procedure is robust against
the non-proportional hazards assumption. We propose nonparametric Bayeisan
(BNP) estimators for RMST using a dependent stick-breaking process prior
mixture model that adjusts for mixed-type covariates. The proposed Bayesian
estimators can yield both group-level causal estimate and subject-level
predictions. Besides, we propose a novel dependent stick-breaking process prior
that on average results in narrower credible intervals while maintaining
similar coverage probability compared to a dependent probit stick-breaking
process prior. We conduct simulation studies to investigate the performance of
the proposed BNP RMST estimators compared to existing frequentist approaches
and under different Bayesian modeling choices. The proposed framework is
applied to estimate the treatment effect of an immuno therapy among KRAS
wild-type colorectal cancer patients.
