Analysis of transformation models with censored data

TLDR

The class of regression models considered includes proportional hazards and proportional odds models. The study proposes semi‑parametric transformation models and generalized estimating equation–based inference procedures to assess covariate effects with censored data. The authors implement these models and inference procedures and evaluate their performance through numerical studies on realistic sample sizes. The resulting models and procedures offer useful alternatives to the Cox regression model in survival analysis.

Abstract

In this paper we consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a class of generalised estimating equations are proposed to examine the covariate effects with censored observations. Numerical studies are conducted to investigate the properties of our proposals for practical sample sizes. These transformation models, coupled with the new simple inference procedures, provide many useful alternatives to the Cox regression model in survival analysis.