Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation

0541888 - ÚI 2022 RIV GB eng J - Journal Article
Selingerová, I. - Katina, Stanislav - Horová, I.
Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation.
Journal of Statistical Computation and Simulation. Roč. 91, č. 13 (2021), s. 2717-2739. ISSN 0094-9655. E-ISSN 1563-5163
Grant - others:Ministerstvo zdravotnictví - GA MZd(CZ) 00209805
Institutional support: RVO:67985807
Keywords : Survival analysis * hazard function * Kernel estimation * simulations * Cox model
OECD category: Statistics and probability
Impact factor: 1.225, year: 2021
Method of publishing: Open access
http://dx.doi.org/10.1080/00949655.2021.1906875

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.
Permanent Link: http://hdl.handle.net/11104/0319382