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|a 2012014570
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|a 9781439872864 (hardback)
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|a 1439872864 (hardback)
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|a QA279
|b .R59 2012eb
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|a 518
|2 23
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| 096 |
|
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|a QA279
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| 100 |
1 |
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|a Rizopoulos, Dimitris.
|
| 245 |
1 |
0 |
|a Joint models for longitudinal and time-to-event data :
|b with applications in R /
|c Dimitris Rizopoulos.
|
| 264 |
|
1 |
|a Boca Raton :
|b CRC Press,
|c 2012.
|
| 300 |
|
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|a 1 online resource (273 pages)
|
| 336 |
|
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Chapman & Hall/CRC biostatistics series ;
|v 6
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| 520 |
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|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"--
|c Provided by publisher.
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| 504 |
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|a Includes bibliographical references and index.
|
| 588 |
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|a Description based on print version record.
|
| 505 |
0 |
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|a 1. Introduction -- 2. Longitudinal data analysis -- 3. Analysis of event time data -- 4. Joint models for longitudinal and time-to-event data -- 5. Extensions of the standard joint model -- 6. Joint model diagnostics -- 7. Prediction and accuracy in joint models.
|
| 500 |
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|a Electronic resource.
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| 650 |
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2 |
|a Numerical Analysis, Computer-Assisted.
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| 650 |
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0 |
|a Numerical analysis
|x Data processing.
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| 650 |
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0 |
|a R (Computer program language)
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| 650 |
|
7 |
|a MATHEMATICS / Probability & Statistics / General.
|2 bisacsh
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| 650 |
|
7 |
|a MEDICAL / Epidemiology.
|2 bisacsh
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| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 710 |
2 |
|
|a ebrary, Inc.
|
| 776 |
0 |
8 |
|a Rizopoulos, Dimitris.
|t Joint models for longitudinal and time-to-event data.
|d Boca Raton : CRC Press, 2012
|z 9781439872864
|w (DLC) 2012014570
|w (OCoLC)698322708
|
| 830 |
|
0 |
|a Chapman & Hall/CRC biostatistics series ;
|v 6.
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/tamucs/detail.action?docID=952042
|y Connect to the full text of this electronic book
|t 0
|
| 948 |
|
|
|a cataloged
|b h
|c 2014/7/17
|d c
|e jlanham
|f 9:00:17 am
|
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|a C0
|b TXA
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|i beaab572-d175-3cf7-95e7-fa31c38bbf78
|t 0
|
| 999 |
|
|
|a MARS
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e QA279 .R59 2012eb
|h Library of Congress classification
|
| 998 |
f |
f |
|a QA279 .R59 2012eb
|t 0
|l Available Online
|