Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00541888" target="_blank" >RIV/67985807:_____/21:00541888 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00209805:_____/21:00078578 RIV/00216224:14310/21:00121410

Výsledek na webu

<a href="http://dx.doi.org/10.1080/00949655.2021.1906875" target="_blank" >http://dx.doi.org/10.1080/00949655.2021.1906875</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/00949655.2021.1906875" target="_blank" >10.1080/00949655.2021.1906875</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/LM2018125" target="_blank" >LM2018125: Banka klinických vzorků</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Statistical Computation and Simulation

ISSN

0094-9655

e-ISSN

1563-5163

Svazek periodika

91

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

23

Strana od-do

2717-2739

Kód UT WoS článku

000638231000001

EID výsledku v databázi Scopus

2-s2.0-85104074971