Comparison of parametric and semiparametric survival regression models with kernel estimation

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Publikace nespadá pod Lékařskou fakultu, ale pod Přírodovědeckou fakultu. Oficiální stránka publikace je na webu muni.cz.

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SELINGEROVÁ Iveta KATINA Stanislav HOROVÁ Ivanka

Rok publikování 2021
Druh Článek v odborném periodiku
Časopis / Zdroj Journal of Statistical Computation and Simulation
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://www.tandfonline.com/doi/full/10.1080/00949655.2021.1906875
Doi http://dx.doi.org/10.1080/00949655.2021.1906875
Klíčová slova Survival analysis; hazard function; Kernel estimation; simulations; Cox model
Popis 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.
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