Comparison of parametric and semiparametric survival regression models with kernel estimation. Issue 13 (2nd September 2021)

• Record Type: Journal Article
• Authors: Selingerova, Iveta; Katina, Stanislav; Horova, Ivanka
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 13(2021)
• Page Start: 2717
• Page End: 2739
• Publication Date: 2021-09-02
• Subjects: Survival analysis -- hazard function -- Kernel estimation -- simulations -- Cox model; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2021.1906875
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : The modelling of censored survival data is based on different estimations of the conditional hazard function. When survival time follows a known distribution, parametric models are useful. This strong assumption is replaced by a weaker in the case of semiparametric models. For instance, the frequently used model suggested by Cox is based on the proportionality of hazards. These models use non-parametric methods to estimate some baseline hazard and parametric methods to estimate the influence of a covariate. An alternative approach is to use smoothing that is more flexible. In this paper, two types of kernel smoothing and some bandwidth selection techniques are introduced. Application to real data shows different interpretations for each approach. The extensive simulation study is aimed at comparing different approaches and assessing their benefits. Kernel estimation is demonstrated to be very helpful for verifying assumptions of parametric or semiparametric models and is able to capture changes in the hazard function in both time and covariate directions.