東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Joint models for longitudinal and ti...

Rizopoulos, Dimitris.

FindBook

Google Book

Amazon

博客來

Joint models for longitudinal and time-to-event data : = with applications in R /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Joint models for longitudinal and time-to-event data :/ Dimitris Rizopoulos.
其他題名:	with applications in R /
作者:	Rizopoulos, Dimitris.
出版者:	Boca Raton :CRC Press, : 2012.,
面頁冊數:	xiv, 261 p. :ill. ;24 cm.
標題:	Numerical analysis - Data processing. -
ISBN:	9781439872864

Joint models for longitudinal and time-to-event data : = with applications in R /
Rizopoulos, Dimitris.

Joint models for longitudinal and time-to-event data :with applications in R /Dimitris Rizopoulos. - Boca Raton :CRC Press,2012. - xiv, 261 p. :ill. ;24 cm. - Chapman & Hall/CRC biostatistics series ;6..

Includes bibliographical references and index.

Preface Joint models for longitudinal and time-to-event data have become a valuable tool in the analysis of follow-up data. These models are applicable mainly in two settings: First, when focus is in the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Due to their capability to provide valid inferences in settings where simpler statistical tools fail to do so, and their wide range of applications, the last 25 years have seen many advances in the joint modeling field. Even though interest and developments in joint models have been widespread, information about them has been equally scattered in articles, presenting recent advances in the field, and in book chapters in a few texts dedicated either to longitudinal or survival data analysis. However, no single monograph or text dedicated to this type of models seems to be available. The purpose in writing this book, therefore, is to provide an overview of the theory and application of joint models for longitudinal and survival data. In the literature two main frameworks have been proposed, namely the random effects joint model that uses latent variables to capture the associations between the two outcomes (Tsiatis and Davidian, 2004), and the marginal structural joint models based on G estimators (Robins et al., 1999, 2000). In this book we focus in the former. Both subfields of joint modeling, i.e., handling of endogenous time-varying covariates and nonrandom dropout, are equally covered and presented in real datasets.

ISBN: 9781439872864GBP55.99

LCCN: 2012014570Subjects--Topical Terms:

532594
Numerical analysis
--Data processing.

LC Class. No.: QA279 / .R59 2012

Dewey Class. No.: 518

Joint models for longitudinal and time-to-event data : = with applications in R /
LDR:02429cam a2200229 a 4500 001 1979305
008 150513s2012 flua b 001 0 eng
010 $a 2012014570
020 $a 9781439872864 $q (hbk.) : $c GBP55.99
020 $a 1439872864 $q (hbk.)
020 $a 9781439872871 $q (ePDF)
020 $a 1439872872 $q (ePDF)
020 $a 1299992692 $q (ebk.)
020 $a 9781299992696 $q (ebk.)
040 $a DLC $c DLC
050 0 0 $a QA279 $b .R59 2012
082 0 0 $a 518 $2 23
100 1 $a Rizopoulos, Dimitris. $3 2121431
245 1 0 $a Joint models for longitudinal and time-to-event data : $b with applications in R / $c Dimitris Rizopoulos.
260 # $a Boca Raton : $b CRC Press, $c 2012.
300 $a xiv, 261 p. : $b ill. ; $c 24 cm.
490 1 0 $a Chapman & Hall/CRC biostatistics series ; $v 6.
504 $a Includes bibliographical references and index.
520 # $a Preface Joint models for longitudinal and time-to-event data have become a valuable tool in the analysis of follow-up data. These models are applicable mainly in two settings: First, when focus is in the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Due to their capability to provide valid inferences in settings where simpler statistical tools fail to do so, and their wide range of applications, the last 25 years have seen many advances in the joint modeling field. Even though interest and developments in joint models have been widespread, information about them has been equally scattered in articles, presenting recent advances in the field, and in book chapters in a few texts dedicated either to longitudinal or survival data analysis. However, no single monograph or text dedicated to this type of models seems to be available. The purpose in writing this book, therefore, is to provide an overview of the theory and application of joint models for longitudinal and survival data. In the literature two main frameworks have been proposed, namely the random effects joint model that uses latent variables to capture the associations between the two outcomes (Tsiatis and Davidian, 2004), and the marginal structural joint models based on G estimators (Robins et al., 1999, 2000). In this book we focus in the former. Both subfields of joint modeling, i.e., handling of endogenous time-varying covariates and nonrandom dropout, are equally covered and presented in real datasets.
650 # 0 $a Numerical analysis $x Data processing. $3 532594
650 # 0 $a R (Computer program language). $3 740890