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Author: Menglu Liang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recurrent events are commonly observed in follow-up studies where the observation could be terminated by drop-out or a terminal event (e.g., death). In this dissertation, we focused on joint modelling approaches for handling recurrent and terminal events. Specifically, in our proposed joint modeling, the residual dependence of recurrent and failure event processes is modelled through a copula function after adjusted for subject-level frailties. The models have been extended for general applications to dynamically capture time-varying unobserved characteristics by allowing the copula parameters to be time-dependent, thus beneficial for precision medicine and risk prediction. We developed comprehensive strategies to quantify the prediction accuracy of our proposed modeling regarding the terminal behaviours. We also evaluated the performance of our proposal in both internal and external validation. As a particular application of our methods in the studies of dynamic prediction of disease, the rate of recurrent events could start to shift, accelerate or decelerate at a specific time prior to the occurrence of a primary failure event. Given accumulative history of recurrent data up to pre-specific landmark time, our model can be dynamically estimate the survival probability for subjects in the risk set at any subsequent post-landmark time points. We also further generalized our models to analyze multiple-type recurrent events data subject to a terminal event. Joint modelling multi-modal events altogether is challenging because of complex data structure and incomplete data due to (informative) censoring, where multivariate recurrent events are usually correlated and they are also associated with the failure time. In our work, we quantified the correlation between different types of recurrent events and the one between these recurrent and failure-time processes by considering multiple latent (frailty) terms and also a multivariate copula function. The properties of the parameter estimates and their empirical performances via simulation studies and real data applications were exclusively explored. In addition, the dynamic prediction based upon combined and cumulative history of recurrent events from joint modeling was performed.
Author: Menglu Liang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recurrent events are commonly observed in follow-up studies where the observation could be terminated by drop-out or a terminal event (e.g., death). In this dissertation, we focused on joint modelling approaches for handling recurrent and terminal events. Specifically, in our proposed joint modeling, the residual dependence of recurrent and failure event processes is modelled through a copula function after adjusted for subject-level frailties. The models have been extended for general applications to dynamically capture time-varying unobserved characteristics by allowing the copula parameters to be time-dependent, thus beneficial for precision medicine and risk prediction. We developed comprehensive strategies to quantify the prediction accuracy of our proposed modeling regarding the terminal behaviours. We also evaluated the performance of our proposal in both internal and external validation. As a particular application of our methods in the studies of dynamic prediction of disease, the rate of recurrent events could start to shift, accelerate or decelerate at a specific time prior to the occurrence of a primary failure event. Given accumulative history of recurrent data up to pre-specific landmark time, our model can be dynamically estimate the survival probability for subjects in the risk set at any subsequent post-landmark time points. We also further generalized our models to analyze multiple-type recurrent events data subject to a terminal event. Joint modelling multi-modal events altogether is challenging because of complex data structure and incomplete data due to (informative) censoring, where multivariate recurrent events are usually correlated and they are also associated with the failure time. In our work, we quantified the correlation between different types of recurrent events and the one between these recurrent and failure-time processes by considering multiple latent (frailty) terms and also a multivariate copula function. The properties of the parameter estimates and their empirical performances via simulation studies and real data applications were exclusively explored. In addition, the dynamic prediction based upon combined and cumulative history of recurrent events from joint modeling was performed.
Author: Dimitris Rizopoulos
Publisher: CRC Press
ISBN: 1439872864
Category : Mathematics
Languages : en
Pages : 279
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Book Description
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/
Author: Melinda Mills
Publisher: SAGE
ISBN: 1446243303
Category : Social Science
Languages : en
Pages : 302
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Book Description
Introducing Survival Analysis and Event History Analysis is an accessible, practical and comprehensive guide for researchers and students who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ′hands-on′ exercises and resources for both students and instructors, Introducing Survival Analysis and Event History Analysis allows researchers to quickly master these advanced statistical techniques. This book is written from the perspective of the ′user′, making it suitable as both a self-learning tool and graduate-level textbook. Introducing Survival Analysis and Event History Analysis covers the most up-to-date innovations in the field, including advancements in the assessment of model fit, frailty and recurrent event models, discrete-time methods, competing and multistate models and sequence analysis. Practical instructions are also included, focusing on the statistical program R and Stata, enabling readers to replicate the examples described in the text. This book comes with a glossary, a range of practical and user-friendly examples, cases and exercises.
Author: Ran Liao
Publisher:
ISBN:
Category :
Languages : en
Pages : 256
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Book Description
Survival analysis often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In this dissertation, we focus on the joint modeling of bivariate time to event data with the estimation of the association parameters and also in the situation of a semi-competing risk. This dissertation contains three related topics on bivariate time to event mod els. The first topic is on estimating the cross ratio which is an association parameter between bivariate survival functions. One advantage of using cross-ratio as a depen dence measure is that it has an attractive hazard ratio interpretation by comparing two groups of interest. We compare the parametric, a two-stage semiparametric and a nonparametric approaches in simulation studies to evaluate the estimation perfor mance among the three estimation approaches. The second part is on semiparametric models of univariate time to event with a semi-competing risk. The third part is on semiparametric models of bivariate time to event with semi-competing risks. A frailty-based model framework was used to accommodate potential correlations among the multiple event times. We propose two estimation approaches. The first approach is a two stage semiparametric method where cumulative baseline hazards were estimated by nonparametric methods first and used in the likelihood function. The second approach is a penalized partial likelihood approach. Simulation studies were conducted to compare the estimation accuracy between the proposed approaches. Data from an elderly cohort were used to examine factors associated with times to multiple diseases and considering death as a semi-competing risk.
Author: Thomas R. Fleming
Publisher: John Wiley & Sons
ISBN: 111815066X
Category : Mathematics
Languages : en
Pages : 454
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Book Description
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians." -Biometrische Zeitschrift "Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text." -Mathematical Reviews "This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature." -Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts "The authors have performed a valuable service to researchers in providing this material in [a] self-contained and accessible form. . . This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis." -Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself.
Author: Hans van Houwelingen
Publisher: CRC Press
ISBN: 1439835438
Category : Mathematics
Languages : en
Pages : 250
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Book Description
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a
Author: Lili Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 161
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Book Description
An important practical problem in the survival analysis is predicting the time to a future event such as the death or failure of a subject. It is of great importance for the medical decision making to investigate how the predictor variables including repeated measurements of the same subjects are affecting the future time-to-event. Such a prediction problem is particularly more challenging due to the fact that the future values of predictor variables are unknown, and they may vary dynamically over time. In this dissertation, we consider a predictive approach based on modeling the forward intensity function. To handle the practical difficulty due to missing data in longitudinal measurements, and to accommodate observations at irregularly spaced time points, we propose a smoothed composite likelihood approach for estimations. The forward intensity function approach intrinsically incorporates the future dynamics in the predictor variables that affect the stochastic occurrence of the future event. Thus the proposed framework is advantageous and parsimonious from requiring no separated modeling step for the stochastic mechanism of the predictor variables. Our theoretical analysis establishes the validity of the forward intensity modeling approach and the smoothed composite likelihood method. To model the parameters as continuous functions of time, we introduce the penalized B-spline method into the proposed approach. Extensive simulations and real-data analyses demonstrate the promising performance of the proposed predictive approach.
Author: Yingwei Peng
Publisher: CRC Press
ISBN: 0429629680
Category : Mathematics
Languages : en
Pages : 268
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Book Description
Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including estimation, inference, and software. This book is useful for statistical researchers and graduate students, and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling, survival models, and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages, SAS macros, and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data, cure modeling for multivariate, recurrent-event, and competing-risks survival data, and joint modeling with longitudinal data, statistical testing for the existence and difference of cure rates and sufficient follow-up, new developments in Bayesian cure models, applications of cure models in public health research and clinical trials.
Author: Luc Duchateau
Publisher: Springer Science & Business Media
ISBN: 038772835X
Category : Mathematics
Languages : en
Pages : 329
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Book Description
Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.
Author: Silvia Avram
Publisher:
ISBN: 9781526421036
Category : Economics
Languages : en
Pages : 0
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Book Description
This entry introduces event history and survival analysis, a set of methods adapted for data relating to the timing and occurrence of events. It starts with a review of the particularities of this type of data and the main concepts used to describe and model it. It then introduces two mathematical concepts that are the building blocks of survival analysis: the hazard rate and survivor functions. Next, it shows how these two concepts can be used to model time-to-event data. It reviews both discrete and continuous time models. Within continuous time models, three subclasses are discussed: proportional hazard models, accelerated failure time models, and the semiparametric Cox model. Finally, more complex topics such as unobserved heterogeneity, competing risks, and repeated events are briefly introduced.
