Every week, we select a recently published Open Access article to feature. This week’s article is from Statistics in Medicine and presents simulation studies to demonstrate Cox models using the maximum penalised likelihood method.
The article’s abstract is given below, with the full article available to read here.
Cox models with time-varying covariates and partly-interval censoring–A maximum penalised likelihood approach. Statistics in Medicine. 2022; 1– 19. doi:10.1002/sim.9645, .
Time-varying covariates can be important predictors when model based predictions are considered. A Cox model that includes time-varying covariates is usually referred to as an extended Cox model. When only right censoring is presented in the observed survival times, the conventional partial likelihood method is still applicable to estimate the regression coefficients of an extended Cox model. However, if there are interval-censored survival times, then the partial likelihood method is not directly available unless an imputation, such as the middle point imputation, is used to replaced the left- and interval-censored data. However, such imputation methods are well known for causing biases. This paper considers fitting of the extended Cox models using the maximum penalised likelihood method allowing observed survival times to be partly interval censored, where a penalty function is used to regularise the baseline hazard estimate. We present simulation studies to demonstrate the performance of our proposed method, and illustrate our method with applications to two real datasets from medical research.More Details