Optimal stratification of survival data via Bayesian nonparametric mixtures

Riccardo Corradin, Luis Enrique Nieto-Barajas and Bernardo Nipoti

Econometrics and Statistics, 2022, vol. 22, issue C, 17-38

Abstract: The stratified proportional hazards model represents a simple solution to take into account heterogeneity within the data while keeping the multiplicative effect of the predictors on the hazard function. Strata are typically defined a priori by resorting to the values of a categorical covariate. A general framework is proposed, which allows the stratification of a generic accelerated lifetime model, including, as a special case, the Weibull proportional hazards model. The stratification is determined a posteriori, taking into account that strata might be characterized by different baseline survivals, and also by different effects of the predictors. This is achieved by considering a Bayesian nonparametric mixture model and the posterior distribution it induces on the space of data partitions. An optimal stratification is then identified following a decision theoretic approach. In turn, stratum-specific inference is carried out. The performance of this method and its robustness to the presence of right-censored observations are investigated through an extensive simulation study. Further illustration is provided analysing a data set from the University of Massachusetts AIDS Research Unit IMPACT Study.

Keywords: Accelerated life model; Bayesian nonparametrics; Normalized inverse Gaussian process; Proportional hazards model; Stratification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:22:y:2022:i:c:p:17-38

DOI: 10.1016/j.ecosta.2021.05.002

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