joint models for longitudinal and time to event data

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Joint Models For Longitudinal And Time To Event Data

Author : Dimitris Rizopoulos
ISBN : 9781439872864
Genre : Mathematics
File Size : 40. 30 MB
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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/

Joint Modeling Of Longitudinal And Time To Event Data

Author : Robert Elashoff
ISBN : 9781439807835
Genre : Mathematics
File Size : 87. 42 MB
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Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues. Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website. This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.

Joint Modeling Of Longitudinal Zero Inflated Count And Time To Event Data A Bayesian Perspective

Author :
ISBN : OCLC:1051953306
Genre :
File Size : 52. 88 MB
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Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-event data by using a Cox proportional hazards model with piecewise constant baseline hazard. We use a Bayesian framework via Markov chain Monte Carlo simulations implemented in the BUGS programming language. Via an extensive simulation study, we apply the joint model and obtain estimates that are more accurate than those of the corresponding independence model. We apply the proposed method to an alpha-tocopherol, beta-carotene lung cancer prevention study.

Joint Models For Longitudinal And Survival Data

Author : Lili Yang
ISBN : OCLC:883412562
Genre : Bayesian statistical decision theory
File Size : 62. 4 MB
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Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second part, we considered the situation when time-to-event outcome is also collected along with multiple longitudinal biomarkers measured until the occurrence of the event or censoring. Joint models for longitudinal and time-to-event data can be used to estimate the association between the characteristics of the longitudinal measures over time and survival time. We developed a maximum-likelihood method to joint model multiple longitudinal biomarkers and a time-to-event outcome. In addition, we focused on predicting conditional survival probabilities and evaluating the predictive accuracy of multiple longitudinal biomarkers in the joint modeling framework. We assessed the performance of the proposed methods in simulation studies and applied the new methods to data sets from two cohort studies.

Handbook Of Missing Data Methodology

Author : Geert Molenberghs
ISBN : 9781439854624
Genre : Mathematics
File Size : 21. 69 MB
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Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

A General Joint Model For Longitudinal Measurements And Competing Risks Survival Data With Heterogenous Random Effects

Author : Xin Huang
ISBN : UCLA:L0100105121
Genre :
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Joint Modeling Of A Longitudinal Biomarker Recurrent Events And A Terminal Event In A Matched Study

Author : Cong Xu
ISBN : OCLC:1005122754
Genre :
File Size : 47. 60 MB
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In longitudinal studies, matched designs are often employed to control the potential confounding effects in the field of biomedical research and public health. It is common to collect both repeated measures of risk factors (e.g., biomarkers) and time-to-event data for each subject, such as recurrent events and death. There are existing standard approaches to model the data separately. Mixed effects models are commonly used to model the association between repeated measures and covariates, which can incorporate the correlation among repeated measures. The Cox PH models or accelerated failure time (AFT) model are often used to estimate the covariate effects on the risk of the event. However, separate modeling may lead to biased results and are less efficient when the two processes are related through some unobserved variables. In many instances, the terminal event of death may prevent the observations and even the occurrence of any further recurrent events, but not vice versa. Thus, the common assumption of independent censoring for recurrent events is violated due to the competing risk of death because these two event processes are often correlated. For instance, if recurrent events (e.g., heart attacks) have a substantially negative effect on health condition, then the hazard for death could be increased. In addition, longitudinal biomarkers are often measured repeatedly over time for investigating their association with the event recurrence or death, thus identifying the candidate biomarker with enhance predictive accuracy is crucial for clinical practice. %Moreover, when the objective is to estimate the hazard of the events (e.g., death, cardiovascular disease) and the impact of prognostic biomarkers on the hazard of the event, a joint analysis taking their dependency into account is needed for valid inference. Motivated by the the Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) study, several challenges are recognized for joint modeling: 1) A certain large portion of subjects may not have any recurrent events during the study period due to non-susceptibility to events or censoring; 2) there exists left-censoring issue for some longitudinal biomarkers due to inherent limit of detection; 3) The correlation within matched cohorts need to be incorporated; 4) the informative censoring due to competing risk of death need to be adjusted. In this dissertation, first, we propose a joint frailty model with zero-inflated recurrent events and death in a matched study, where a matched logistic model is adopted to adjust for structural zero recurrent events. We incorporated two frailties to measure the dependency between subjects within a match pair and that among recurrent events within each individual. By sharing the random effects, two event processes of recurrent events and death are dependent with each other. Furthermore, because of left-censoring of the assay used to quantify the marker, longitudinal data could be complicated by left-censoring of some measures. Next, we propose a joint model of longitudinal biomarkers, recurrent events and death which can accommodate left-censoring biomarkers. The maximum likelihood based approach is applied for parameter estimation, where the Monte Carlo Expectation-Maximization (MCEM) algorithm is adopted and implemented in R. In addition, alternative estimation methods such as Gaussian quadrature (PROC NLMIXED) and a Bayesian approach (PROC MCMC) are also considered for comparison to show our method's superiority. Extensive simulations are conducted and a real data application on acute ischemic studies is provided.

The Evaluation Of Surrogate Endpoints

Author : Tomasz Burzykowski
ISBN : 9780387270807
Genre : Medical
File Size : 36. 74 MB
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Covers the latest research on a sensitive and controversial topic in a professional and well researched manner. Provides practical outlook as well as model guidelines and software tools that should be of interest to people who use the software tools described and those who do not. Related title by Co-author Geert Molenbergh has sold more than 3500 copies world wide. Provides dual viewpoints: from scientists in the industry as well as regulatory authorities.

Analyzing And Modeling Spatial And Temporal Dynamics Of Infectious Diseases

Author : Dongmei Chen
ISBN : 9781118629932
Genre : Medical
File Size : 28. 41 MB
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Longitudinal Data Analysis

Author : Garrett Fitzmaurice
ISBN : 142001157X
Genre : Mathematics
File Size : 65. 65 MB
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Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines. Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.

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