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Joint Models for Longitudinal and Time-to-Event Data: With Applications in R de Dimitris Rizopoulos

Joint Models for Longitudinal and Time-to-Event Data: With Applications in R: Chapman & Hall/CRC Biostatistics Series

Autor Dimitris Rizopoulos
en Limba Engleză Paperback – 21 ian 2023

Adresată studenților la masterat sau doctorat în biostatistică, cercetătorilor din domeniul medical și practicienilor în știința datelor de sănătate, lucrarea Joint Models for Longitudinal and Time-to-Event Data oferă un cadru metodologic esențial pentru înțelegerea relației dintre markerii măsurați repetat și riscul de apariție a unui eveniment. Recomandăm acest volum celor care doresc să depășească analizele separate, adesea părtinitoare, în favoarea unei abordări integrate care reflectă complexitatea datelor clinice reale.

Descoperim aici o structură pedagogică riguroasă. Primele capitole pun bazele prin analiza datelor longitudinale și a celor de tip time-to-event, pentru ca nucleul cărții să fie dedicat modelelor comune (joint models). Autorul, Dimitris Rizopoulos, nu se oprește la teorie, ci explorează extensii ale modelului standard și, crucial pentru validitatea științifică, metode de diagnostic și măsuri de acuratețe predictivă. Comparabil cu Dynamical Biostatistical Models de Daniel Commenges în ceea ce privește rigoarea matematică, volumul de față este însă mult mai aplicat pe fluxul de lucru în limbajul R, oferind cod sursă complet și acces la pachetul dedicat JM.

Suntem de părere că punctul forte al acestei ediții este echilibrul dintre detaliul tehnic și aplicabilitate. Cele 36 de ilustrații și studiile de caz, precum monitorizarea antigenului specific prostatic (PSA) în cancerul de prostată, oferă contextul necesar pentru ca cititorul să poată implementa imediat aceste tehnici în propriile proiecte de cercetare. Este o resursă care transformă conceptele abstracte de biostatistică în instrumente de predicție clinică.

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Specificații

ISBN-13: 9781032477565
ISBN-10: 1032477563
Pagini: 278
Ilustrații: 36
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.44 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Biostatistics Series

Public țintă

Professional Practice & Development

De ce să citești această carte

Această carte este indispensabilă pentru oricine lucrează cu seturi de date medicale complexe unde timpul și măsurătorile repetate se intersectează. Cititorul câștigă nu doar o înțelegere teoretică a modelelor de efecte mixte, ci și capacitatea de a le aplica folosind pachetul R creat de autor. Este un ghid practic care elimină barierele dintre statistica avansată și implementarea software în cercetarea clinică.

Despre autor

Dimitris Rizopoulos este o autoritate recunoscută în domeniul biostatisticii, activând ca profesor la Erasmus University Medical Center din Rotterdam. Expertiza sa principală se concentrează pe dezvoltarea metodelor statistice pentru date longitudinale și analiza supraviețuirii, fiind creatorul pachetului software JM pentru limbajul R. Contribuțiile sale în cadrul seriei Chapman & Hall/CRC Biostatistics Series sunt fundamentale pentru modernizarea modului în care cercetătorii abordează interdependența markerilor biologici și evoluția bolilor.

Cuprins

Introduction. Analysis of Longitudinal Data. Analysis of Time-to-Event Data. Joint Models for Longitudinal and Time-to-Event Data. Extensions of the Standard Joint Model. Diagnostics. Survival Probabilities and Prospective Accuracy Measures.

Recenzii

"Overall, the book provides a nice introduction to joint models and the R package "JM". It is well written, readable, and comprehensive. With the availability of the R package for joint models, it is expected that joint models will become increasingly popular in practice, especially in medical research. In summary, the book makes an important contribution to the research and application of joint models."
—Lang Wu, Department of Statistics, The University of British Columbia, Vancouver, Canada, in the Journal of Biopharmaceutical Statistics
"The book is well written in a matter-of-fact style that makes even unfamiliar readers understand the concept of joint models and furthermore provides them with a guide for getting started with their own analysis. The more joint model-savvy reader will, on the other hand, find inspiration for further foraging into the subject of model extensions, diagnostics, prediction, and accuracy. … a handy guide for anyone with a need to analyze survival data in the presence of a time-dependent covariate that is measured several times. As the author incorporates a longitudinal model for such a covariate into the relative risk regression modeling framework, we observe the advantage of being able to account for measurement errors within our covariate; a fortification of our research outcomes. All in all a satisfying book on joint models with a solid payout for fellow researchers."
—Maral Saadati, Biometrical Journal, 55, 2013
"This new addition to the genre is based on the JM package written by the author and has been done well. … I particularly liked the sections on numerical methods, which manage to give a useful overview of what the package is actually doing but without scaring off the mathematically reluctant. The dreaded problem of non-convergence is met head-on, with an illustration and discussion of how a little knowledge of the fitting algorithms can help to overcome such problems. This alone is worth the price of the book! … To summarize, this is a very well-crafted introduction to an active research area that I would recommend to anyone interested in getting into this field or in learning to analyze such data."
—Geoff Jones, Australian & New Zealand Journal of Statistics, 2013

"The book is well written in a matter-of-fact style that makes even unfamiliar readers understand the concept of joint models and furthermore provides them with a guide for getting started with their own analysis. The more joint model-savvy reader will, on the other hand, find inspiration for further foraging into the subject of model extensions, diagnostics, prediction, and accuracy. … a handy guide for anyone with a need to analyze survival data in the presence of a time-dependent covariate that is measured several times. As the author incorporates a longitudinal model for such a covariate into the relative risk regression modeling framework, we observe the advantage of being able to account for measurement errors within our covariate; a fortification of our research outcomes. All in all a satisfying book on joint models with a solid payout for fellow researchers."
—Maral Saadati, Biometrical Journal, 55, 2013
"This new addition to the genre is based on the JM package written by the author and has been done well. … I particularly liked the sections on numerical methods, which manage to give a useful overview of what the package is actually doing but without scaring off the mathematically reluctant. The dreaded problem of non-convergence is met head-on, with an illustration and discussion of how a little knowledge of the fitting algorithms can help to overcome such problems. This alone is worth the price of the book! … To summarize, this is a very well-crafted introduction to an active research area that I would recommend to anyone interested in getting into this field or in learning to analyze such data."
—Geoff Jones, Australian & New Zealand Journal of Statistics, 2013

Descriere scurtă

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models.


All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.



All the R code used in the book is available at:


http://jmr.r-forge.r-project.org/



Notă biografică

Dimitris Rizopoulos is an Assistant Professor at the Department of Biostatistics of the Erasmus University Medical Center in the Netherlands. Dr. Rizopoulos received his M.Sc. in Statistics in 2003 from the Athens University of Economics and Business, and a Ph.D. in Biostatistics in 2008 from the Katholieke Universiteit Leuven.


Dr. Rizopoulos wrote his dissertation, as well as a number of methodological articles on various aspects of joint models for longitudinal and time-to-event data. He currently serves as an Associate Editor for Biometrics and Biostatistics, and has been a guest editor for a special issue in joint modeling techniques in Statistical Methods in Medical Research.