Semiparametric Varying Coefficient Mixed Effects Modeling Approaches To Longitudinal Data

Download Semiparametric Varying Coefficient Mixed Effects Modeling Approaches To Longitudinal Data full books in PDF, epub, and Kindle. Read online Semiparametric Varying Coefficient Mixed Effects Modeling Approaches To Longitudinal Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Semiparametric Varying-coefficient Mixed Effects Modeling Approaches to Longitudinal Data

Author :
Publisher :
ISBN 13 :
Total Pages : 126 pages
Book Rating : 4.:/5 (974 download)

DOWNLOAD NOW!

Book Synopsis Semiparametric Varying-coefficient Mixed Effects Modeling Approaches to Longitudinal Data by :

Download or read book Semiparametric Varying-coefficient Mixed Effects Modeling Approaches to Longitudinal Data written by and published by . This book was released on 2015 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Longitudinal (panel) studies focus on the models and data that arise from repeated measurements taken from a group of subjects. In this dissertation, we consider mixed effects modeling approaches with nonparametric estimation methods to model the dynamics in the longitudinal data, and explore the study in three focused research topics. Firstly, motivated by establishing a reliable quantitative investigation of the inequalities in developing countries, we use China’s health care expenditure data as an example, and adapt a semi-parametric varying-coefficients spatial panel data model. A profile likelihood based estimation procedure with a fully iterated two-step local estimation method is proposed to estimate the varying coefficients, spatial specific effects, and spatial autoregressive parameter. Simulation studies are conducted to examine the performance of the proposed estimators. The simulation results indicate our methods work very well. Comparison of the estimated coefficients indicates that the semi-parametric spatial varying coefficient panel data model surpasses the parametric one, confirming that the estimated coefficients are time-varying and can better describe the impact of those important factors. We secondly investigate human immunodeficiency virus (HIV) dynamic modeling. HIV dynamic models have been introduced to characterize short-term acquired immune deficiency syndrome (AIDS) treatment. However, in long-term HIV dynamics, viral load will typically increase over the treatment period due to the development of drug resistance. Time-varying drug resistance can be incorporated into the ordinary differential equations (ODEs) model, but often has no close form solution. In clinical practice, only viral load and CD4+ T-cell counts can be censored with measurement errors due to technical constraints. In this study, we consider a mixed-effects nonlinear parametric model describing short-term longitudinal AIDS treatment, along with a set of mixed-effects ODE models fitting long-term longitudinal AIDS clinical trial data. We propose a multi-stage estimation procedure to estimate the mean constant dynamic parameters and time-varying curve functions, and also quantify individual heterogeneity among subjects. Bootstrap confidence intervals for the constant dynamic parameters are calculated to evaluate the estimation procedure. The finite sample properties of the proposed estimator are studied via simulations and the methodology is also applied to a longitudinal AIDS clinical data set. The results suggest that the proposed estimation procedure is effective and appropriate to estimate both individual constant dynamic parameters and time-varying curve functions in long-term HIV dynamic models. Finally, we propose three deterministic linear dynamic models in general forms describing longitudinal dynamic systems, which are designed to be applicable across multiple disciplines. The proposed models are characterized by a set of ODEs in which all state variables in the system are used to interpret each of their derivatives. In this study, we consider multi-stage smoothing-based and mixed effects modeling approaches to estimate both unknown functional varying coefficients and constant parameters. Further, we develop a cross validation based method to identify the parametric and nonparametric components in the proposed models, and show two computational algorithms for illustrations. A simulation study of the FitzHugh-Nagumo system, a biophysical two dimension model of neuronal spike generation, is conducted. The results indicate the importance of data quality for achieving reliable estimates of dynamic parameters. Nonetheless, good estimates can still be obtained from large noise data given a large number of time points with sufficient subjects.

Nonparametric Regression Methods for Longitudinal Data Analysis

Author : Hulin Wu
Publisher : John Wiley & Sons
ISBN 13 : 0470009667
Total Pages : 401 pages
Book Rating : 4.4/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Regression Methods for Longitudinal Data Analysis by : Hulin Wu

Download or read book Nonparametric Regression Methods for Longitudinal Data Analysis written by Hulin Wu and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Nonparametric Models for Longitudinal Data

Author : Colin O. Wu
Publisher : CRC Press
ISBN 13 : 0429939086
Total Pages : 552 pages
Book Rating : 4.4/5 (299 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Models for Longitudinal Data by : Colin O. Wu

Download or read book Nonparametric Models for Longitudinal Data written by Colin O. Wu and published by CRC Press. This book was released on 2018-05-23 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: Provides an overview of parametric and semiparametric methods Shows smoothing methods for unstructured nonparametric models Covers structured nonparametric models with time-varying coefficients Discusses nonparametric shared-parameter and mixed-effects models Presents nonparametric models for conditional distributions and functionals Illustrates implementations using R software packages Includes datasets and code in the authors’ website Contains asymptotic results and theoretical derivations

Mixed Effects Models for Complex Data

Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)

DOWNLOAD NOW!

Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu

Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Longitudinal Data Analysis

Author : Garrett Fitzmaurice
Publisher : CRC Press
ISBN 13 : 142001157X
Total Pages : 633 pages
Book Rating : 4.4/5 (2 download)

DOWNLOAD NOW!

Book Synopsis Longitudinal Data Analysis by : Garrett Fitzmaurice

Download or read book Longitudinal Data Analysis written by Garrett Fitzmaurice and published by CRC Press. This book was released on 2008-08-11 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Hierarchical Time-varying Mixed-effects Models in High-dimensional Time Series and Longitudinal Data Studies

Author : Jinglan Li
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (11 download)

DOWNLOAD NOW!

Book Synopsis Hierarchical Time-varying Mixed-effects Models in High-dimensional Time Series and Longitudinal Data Studies by : Jinglan Li

Download or read book Hierarchical Time-varying Mixed-effects Models in High-dimensional Time Series and Longitudinal Data Studies written by Jinglan Li and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consider a varying coefficient model (Hastie and Tibshirani, 1993), where the coefficient is unknown but is dynamic in the sense that it is a function of a certain covariate. In some cases, the covariate is a special variable 'time'. Motivated by the need for varying-coefficient vector time series models (Jiang, 1999) and varying-coefficient partially linear models (Fan, Huang, and Li, 2007), we are primarily interested in time-varying coefficient models for continuous multivariate time series data and continuous longitudinal data. The challenge is how to simultaneously display serial, clustering, and multivariate attributes of the data set, to which the routinely assumed two-level and univariate response models are not able to apply. We approach this problem by a flexible new model called multiple response hierarchical time-varying mixed-effects model. So far, the thesis has focused on two responses. Extension to>2 responses involves no fundamentally new ideas. The model first uses varying-coefficient parameters for accurately describing the dynamic of the series. The new covariance matrix is decomposed into between-response correlation structure of random cluster effect and correlation structure between measurement errors. By allowing shared cluster effects the model allows for characterizing homogeneity in repeated measurements in the same cluster. By allowing for time dependent error terms, it is possible to model the correlation induced by within-subject variation. We adopt a similar approach of Fan and Gijbels (1996), where we first propose local linear regression estimators for the varying coefficients, and then obtain random effect prediction by maximizing the profile likelihood with a closed-form solution. Asymptotic results give good insight into the properties of estimators. It is shown that estimates are consistent. We also conduct the model comparison, it turns out that the proposed methods outperform the traditional univariate response models, nonparametric models, and linear mixed effects models in both predicting the response and estimating the coefficient surface based on simulation studies. Finally, we have applied this model to a real-world study on the price-volume relation of NASDAQ stock market data.

Varying-coefficient Models for Longitudinal Data: Piecewise-continuous, Flexible, Mixed-effects Models and Methods for Analyzing Data with Nonignorable Dropout

Download Varying-coefficient Models for Longitudinal Data: Piecewise-continuous, Flexible, Mixed-effects Models and Methods for Analyzing Data with Nonignorable Dropout PDF Online Free

Author : Jeri E. Forster
Publisher :
ISBN 13 : 9781109838824
Total Pages : 178 pages
Book Rating : 4.8/5 (388 download)

DOWNLOAD NOW!

Book Synopsis Varying-coefficient Models for Longitudinal Data: Piecewise-continuous, Flexible, Mixed-effects Models and Methods for Analyzing Data with Nonignorable Dropout by : Jeri E. Forster

Download or read book Varying-coefficient Models for Longitudinal Data: Piecewise-continuous, Flexible, Mixed-effects Models and Methods for Analyzing Data with Nonignorable Dropout written by Jeri E. Forster and published by . This book was released on 2006 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: We address two challenges specific to longitudinal data. Firstly, the relationship between continuous outcome measures and longitudinally collected predictors may vary over time. To flexibly model these relationships and generate interpretable results, we develop mixed-effects, piecewise-continuous varying-coefficient methods (MPV). These techniques will increase goodness-of-fit and hence improve understanding of these dynamic relationships, aiding in hypothesis generation. We apply these MPV models to immunologic and virologic outcome measures collected in HIV/AIDS clinical trials. Secondly, we propose varying-coefficient methods using natural cubic B-spline basis functions (VCM NS) to semiparametrically model the outcome-dropout relationship in clinical trials where nonignorable dropout is present. These methods are computationally stable, highly flexible and relatively simple to implement. Furthermore, we have control over the amount of flexibility applied to the dropout mechanism. As nonignorable dropout frequently exists and naive methods yield biased results, these are valuable qualities for an effective method. We apply the VCM NS and comparable available methods to an HIV/AIDS clinical trial that shows evidence of nonignorable dropout. In addition, we conduct simulation studies to evaluate performance and compare methodologies. The simulation studies suggest that the VCMNS is an improvement over existing methods when the dropout mechanism is nonlinear.

Nonparametric Regression Analysis of Longitudinal Data

Author : Hans-Georg Müller
Publisher : Springer Science & Business Media
ISBN 13 : 1461239265
Total Pages : 208 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Regression Analysis of Longitudinal Data by : Hans-Georg Müller

Download or read book Nonparametric Regression Analysis of Longitudinal Data written by Hans-Georg Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph reviews some of the work that has been done for longitudi nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work. Completeness is not attempted, neither in the text nor in the references. The following persons have been particularly generous in sharing research or giving advice: Th. Gasser, P. Ihm, Y. P. Mack, V. Mammi tzsch, G . G. Roussas, U. Stadtmuller, W. Stute and R.

Applied Longitudinal Analysis

Author : Garrett M. Fitzmaurice
Publisher : John Wiley & Sons
ISBN 13 : 1118551796
Total Pages : 758 pages
Book Rating : 4.1/5 (185 download)

DOWNLOAD NOW!

Book Synopsis Applied Longitudinal Analysis by : Garrett M. Fitzmaurice

Download or read book Applied Longitudinal Analysis written by Garrett M. Fitzmaurice and published by John Wiley & Sons. This book was released on 2012-10-23 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition ". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis." —Journal of the American Statistical Association Features newly developed topics and applications of the analysis of longitudinal data Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences. The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including: Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material. With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.

Macroeconometrics and Time Series Analysis

Author : Steven Durlauf
Publisher : Springer
ISBN 13 : 0230280838
Total Pages : 417 pages
Book Rating : 4.2/5 (32 download)

DOWNLOAD NOW!

Book Synopsis Macroeconometrics and Time Series Analysis by : Steven Durlauf

Download or read book Macroeconometrics and Time Series Analysis written by Steven Durlauf and published by Springer. This book was released on 2016-04-30 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

Modeling Longitudinal Data

Author : Robert E. Weiss
Publisher : Springer Science & Business Media
ISBN 13 : 0387283145
Total Pages : 445 pages
Book Rating : 4.3/5 (872 download)

DOWNLOAD NOW!

Book Synopsis Modeling Longitudinal Data by : Robert E. Weiss

Download or read book Modeling Longitudinal Data written by Robert E. Weiss and published by Springer Science & Business Media. This book was released on 2006-12-06 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.

Practical Nonparametric and Semiparametric Bayesian Statistics

Author : Dipak D. Dey
Publisher : Springer Science & Business Media
ISBN 13 : 1461217326
Total Pages : 376 pages
Book Rating : 4.4/5 (612 download)

DOWNLOAD NOW!

Book Synopsis Practical Nonparametric and Semiparametric Bayesian Statistics by : Dipak D. Dey

Download or read book Practical Nonparametric and Semiparametric Bayesian Statistics written by Dipak D. Dey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.

Topics on Nonparametric Methods for Longitudinal Data Analysis and Jumps Detection

Author : Shengji Jia
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (113 download)

DOWNLOAD NOW!

Book Synopsis Topics on Nonparametric Methods for Longitudinal Data Analysis and Jumps Detection by : Shengji Jia

Download or read book Topics on Nonparametric Methods for Longitudinal Data Analysis and Jumps Detection written by Shengji Jia and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we investigate the nonparametric methods applied for longitudinal data analysis and jumps detection. The first part is about efficient semi-parametric regression for longitudinal data with nonparametric covariance estimation. Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of errors. But challenges arise in estimating the covariance matrix of longitudinal data collected at irregular or unbalanced time points. We develop a regularization method for estimating the covariance function and a stepwise procedure for estimating the parametric components efficiently in the varying-coefficient partially linear model. This procedure is also applicable to the varying-coefficient temporal mixed effects model. Our method utilizes the structure of the covariance function and thus has faster rates of convergence in estimating the covariance functions and outperforms the existing approaches. The second part is about adaptive jumps detection via nonparametric screening and multiple testing procedure. In many applications, it may appear that a regression function is smooth except at several points where jump discontinuities occur. But challenges arise when the number of jumps is quite large and unknown. We develop a jumps detection procedure via nonparametric screening and multiple testing. The candidates of jumps are first detected through screening and then a multiple testing procedure is applied to rule out the noises. Our proposed method is quite robust in jumps detection and doesn't depend on the choice of tuning parameter and threshold in the screening procedure. All the two procedures are easy to implement and their numerical performance are investigated using both simulated and real data.

A Life Course Perspective on Health Trajectories and Transitions

Author : Claudine Burton-Jeangros
Publisher : Springer
ISBN 13 : 331920484X
Total Pages : 215 pages
Book Rating : 4.3/5 (192 download)

DOWNLOAD NOW!

Book Synopsis A Life Course Perspective on Health Trajectories and Transitions by : Claudine Burton-Jeangros

Download or read book A Life Course Perspective on Health Trajectories and Transitions written by Claudine Burton-Jeangros and published by Springer. This book was released on 2015-08-11 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time. The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population.

Joint Models for Longitudinal and Time-to-Event Data

Author : Dimitris Rizopoulos
Publisher : CRC Press
ISBN 13 : 1439872864
Total Pages : 279 pages
Book Rating : 4.4/5 (398 download)

DOWNLOAD NOW!

Book Synopsis Joint Models for Longitudinal and Time-to-Event Data by : Dimitris Rizopoulos

Download or read book Joint Models for Longitudinal and Time-to-Event Data written by Dimitris Rizopoulos and published by CRC Press. This book was released on 2012-06-22 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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/

Semiparametric Approaches to Inference in Joint Models for Longitudinal and Time-to-event Data

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (656 download)

DOWNLOAD NOW!

Book Synopsis Semiparametric Approaches to Inference in Joint Models for Longitudinal and Time-to-event Data by :

Download or read book Semiparametric Approaches to Inference in Joint Models for Longitudinal and Time-to-event Data written by and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Interest may focus on inference on the longitudinal data process, which is informatively censored by death or withdrawal, or on the hazard relationship. Several methods for fitting such models have been proposed, including regression calibration and likelihood or Bayesian methods. However, most approaches require a parametric distributional assumption (normality) on the random effects. In addition, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. We extend this technique to multiple, possibly correlated, time-dependent covariates. This approach is easy to compute. However, the conditional score approach might be less efficient relative to the likelihood approaches. In addition, inference on the longitudinal data process is not available. To improve the efficiency and meanwhile obtain an estimator for the random effects distribution, we propose to approximate the random effects distribution by the seminonparametric (SNP) densities of Gallant and Nychka (1987), which requires only the assumption that the random effects have a "smooth" density, and take a semiparametric likelihood approach. The EM algorithm is used for implementation. We demonstrate the approaches via simulations and apply th.

Practical Longitudinal Data Analysis

Author : David J. Hand
Publisher : Routledge
ISBN 13 : 1351422650
Total Pages : 248 pages
Book Rating : 4.3/5 (514 download)

DOWNLOAD NOW!

Book Synopsis Practical Longitudinal Data Analysis by : David J. Hand

Download or read book Practical Longitudinal Data Analysis written by David J. Hand and published by Routledge. This book was released on 2017-10-06 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.