Semiparametric transformation models of survival-out-of-hospital

Liuquan Sun (Institute of Applied Mathematics, Academy of Mathematics and Systems Science, C.A.S., Beijing, China; and University of Chinese Academy of Sciences, Beijing, China)

Abstract

Recurrent event data with a terminal event commonly arise in biomedical studies, and the survival-out-of-hospital process is a useful alternative framework for the analysis of recurrent/terminal event data with non-negligible event duration. In this article, we propose a class of semiparametric transformation models for the survival-out-of-hospital process, and the proposed models offer great flexibility in formulating covariate effects on the probability of survival-out-of-hospital. Estimating equation approaches are developed for the model parameters, and the asymptotic properties of the resulting estimators are established. The finite sample performance of the proposed estimators is examined through simulation studies. An application to a Centers for Medicare and Medicaid Services study is provided.

Keywords

estimating equations, multiple imputation, recurrent event, terminal event, transformation model, survival-out-of-hospital

2010 Mathematics Subject Classification

Primary 62N01, 62N02. Secondary 62F12.

Full Text (PDF format)

This research was supported by the Fundamental Research Funds for the Central Universities (2021JBM045), and by the National Natural Science Foundation of China (Grant Nos. 12171463, 11771431 and 11690015).

Received 20 August 2021

Accepted 27 November 2021

Published 4 March 2022