A semiparametric Cox-Aalen transformation model with censored data.

ABSTRACT: We propose a broad class of so-called Cox-Aalen transformation models that incorporate both multiplicative and additive covariate effects on the baseline hazard function within a transformation. The proposed models provide a highly flexible and versatile class of semiparametric models that include the transformation models and the Cox-Aalen model as special cases. Specifically, it extends the transformation models by allowing potentially time-dependent covariates to work additively on the baseline hazard and extends the Cox-Aalen model through a predetermined transformation function. We propose an estimating equation approach and devise an expectation-solving (ES) algorithm that involves fast and robust calculations. The resulting estimator is shown to be consistent and asymptotically norm

SUBMITTER: Ning X 

PROVIDER: S-EPMC10764654 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature