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Likelihood methods in survival analysis :with R examples /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Likelihood methods in survival analysis :Jun Ma, Annabel Webb and Harold Malcolm Hudson.
Reminder of title:	with R examples /
Author:	Ma, Jun
other author:	Webb, Annabel,
Published:	Boca Raton :CRC Press,2025.
Description:	xv, 384 p. :ill. ;25 cm.
Subject:	Survival analysis (Biometry)
ISBN:	9780815362845 :

Likelihood methods in survival analysis :with R examples /
Ma, Jun(Statistics teacher)

Likelihood methods in survival analysis :with R examples /Jun Ma, Annabel Webb and Harold Malcolm Hudson. - 1st ed. - Boca Raton :CRC Press,2025. - xv, 384 p. :ill. ;25 cm. - Chapman & Hall/CRC Biostatistics series. - Chapman & Hall/CRC Biostatistics series..

Includes bibliographical references and index.

"Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes"--

ISBN: 9780815362845 :NT$3885

LCCN: 2024017741Subjects--Topical Terms:

182534
Survival analysis (Biometry)

LC Class. No.: QH323.5 / .M23 2025

Dewey Class. No.: 610.72/7

Likelihood methods in survival analysis :with R examples /
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020 $a 9781032468143 (pbk.)
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050 0 0 $a QH323.5 $b .M23 2025
082 0 0 $a 610.72/7 $2 23/eng/20241101
100 1 $a Ma, Jun $c (Statistics teacher) $e author. $3 1011676
245 1 0 $a Likelihood methods in survival analysis : $b with R examples / $c Jun Ma, Annabel Webb and Harold Malcolm Hudson.
250 $a 1st ed.
260 $a Boca Raton : $b CRC Press, $c 2025.
300 $a xv, 384 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC Biostatistics series
504 $a Includes bibliographical references and index.
520 $a "Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes"-- $c Provided by publisher.
650 0 $a Survival analysis (Biometry) $3 182534
650 0 $a Estimation theory. $3 181864
650 0 $a R (Computer program language) $3 210846
700 1 $a Webb, Annabel, $e author. $3 1011677
700 1 $a Hudson, Harold Malcolm, $e author. $3 1011678
830 0 $a Chapman & Hall/CRC Biostatistics series. $3 1011679