We study a class of the semiparametric Cox-Aalen transformation models for regression analysis. These models accommodate both multiplicative and additive covariate effects within a transformation, while also allowing for potentially time-dependent covariates. Moreover, this class of models includes many popular models such as the semiparametric transformation model, the Cox-Aalen model and the stratified Cox model as special cases. To facilitate efficient computation, we formulate a set of estimating equations and propose an Expectation-Solving (ES) algorithm that guarantees stability and rapid convergence. Under mild regularity assumptions, the resulting estimator is shown to be consistent and asymptotically normal. The validity of the weighted bootstrap is also established. Finally, the proposed method is applied to analyse data from a randomized HIV/AIDS trial.

Papers/Articles

Xi Ning is an Assistant Professor of Statistics at Colby College, United States, where she has been since July 2023. She received her PhD in Applied Mathematics (Statistics concentration) from the University of North Carolina at Charlotte in 2023. Her research focuses on survival analysis and semiparametric modelling, motivated by applications in biomedical studies and clinical trials, with theoretical contributions to semiparametric inference. She is also exploring the integration of machine learning methods in her research. She received the 2023 Eastern North American Region (ENAR) Distinguished Student Paper Award and the 2023 Lifetime Data Science Conference Student Poster Award, organized by the American Statistical Association (ASA).

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