Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation

Result code in 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>
Alternative codes found
RIV/00209805:_____/21:00078578 RIV/00216224:14310/21:00121410
Result on the web
<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>

Result language
angličtina
Original language name
Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/LM2018125" target="_blank" >LM2018125: Bank of Clinical Samples</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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