Download or read book Linear Regression Analysis for Multivariate Failure Time Observations written by Jin-Sying Lin and published by . This book was released on 1991 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Statistical Analysis of Multivariate Failure Time Data written by Ross L. Prentice and published by CRC Press. This book was released on 2019-05-14 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Download or read book Statistical Analysis of Multivariate Interval censored Failure Time Data written by Man-Hua Chen and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. In these cases, each study subject is examined or observed periodically, thus the observed failure time falls into a certain interval. Additional problems arise in the analysis of multivariate interval-censored failure time data. These include the estimating the correlation among failure times. The first part of this dissertation considers regression analysis of multivariate interval-censored failure time data using the proportional odds model. One situation in which the proportional odds model is preferred is when the covariate effects diminish over time. In contrast, if the proportional hazards model is applied for the situation, one may have to deal with time-dependent covariates. We present an inference approach for fitting the model to multivariate interval-censored failure time data. Simulation studies are conducted and an AIDS clinical trial is analyzed by using this methodology. The second part of this dissertation is devoted to the additive hazards model for multivariate interval-censored failure time data. In many applications, the proportional hazards model may not be appropriate and the additive hazards model provides an important and useful alternative. The presented estimates of regression parameters are consistent and asymptotically normal and a robust estimate of their covariance matrix is given that takes into account the correlation of the survival variables. Simulation studies are conducted for practical situations. The third part of this dissertation discusses regression analysis of multivariate interval censored failure time data using the frailty model approach. Based on the most commonly used regression model, the proportional hazards model, the frailty model approach considers the random effect directly models the correlation between multivariate failure times. For the analysis, we will focus on current status or case I interval-censored data and the maximum likelihood approach is developed for inference. The simulation studies are conducted to asses and compare the finite-sample behaviors of the estimators and we apply the proposed method to an animal tumorigenicity experiment.

Download or read book The Statistical Analysis of Multivariate Failure Time Data written by Ross L. Prentice and published by CRC Press. This book was released on 2019-05-14 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Download or read book The Statistical Analysis of Failure Time Data written by John D. Kalbfleisch and published by John Wiley & Sons. This book was released on 2011-01-25 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains additional discussion and examples on left truncationas well as material on more general censoring and truncationpatterns. Introduces the martingale and counting process formulation swillbe in a new chapter. Develops multivariate failure time data in a separate chapterand extends the material on Markov and semi Markovformulations. Presents new examples and applications of data analysis.

Download or read book Survival Analysis State of the Art written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.

Download or read book Statistical Analysis of Multivariate Interval censored Failure Time Data written by Lianming Wang and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-censored failure time data commonly arise in clinical trials and medical studies. In such studies, the failure time of interest is often not exactly observed, but known to fall within some interval. For multivariate interval-censored data, each subject may experience multiple events, each of which is interval-censored. This thesis studies four research problems related to regression analysis and association study of multivariate interval-censored data. In particular, in Chapter 2, we propose a goodness-of-fit test for the marginal Cox model approach, which is the most commonly, used approach in multivariate regression analysis. Chapter 3 presents a two-stage estimation procedure for the association parameter for case 2 bivariate interval-censored data. In Chapter 4 we give a simple procedure to estimate the regression parameter for case 2 interval-censored data and Chapter 5 studies the efficient estimation of regression parameters and association parameter simultaneously for bivariate current status data. All the proposed methods are assessed by simulation studies and illustrated using real-life applications.

Download or read book Recent Advances in Clinical Trial Design and Analysis written by Peter F. Thall and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical trials have two purposes -- to treat the patients in the trial, and to obtain information which increases our understanding of the disease and especially how patients respond to treatment. Statistical design provides a means to achieve both these aims, while statistical data analysis provides methods for extracting useful information from the trial data. Recent advances in statistical computing have enabled statisticians to implement very rapidly a broad array of methods which previously were either impractical or impossible. Biostatisticians are now able to provide much greater support to medical researchers working in both clinical and laboratory settings. As our collective toolkit of techniques for analyzing data has grown, it has become increasingly difficult for biostatisticians to keep up with all the developments in our own field. Recent Advances in Clinical Trial Design and Analysis brings together biostatisticians doing cutting-edge research and explains some of the more recent developments in biostatistics to clinicians and scientists who work in clinical trials.

Download or read book Regression Analysis for Correlated Failure Time Data written by Eric Weijun Lee and published by . This book was released on 1991 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Regression Analysis for Highly Stratified Failure Time Observations written by L. J. Wei and published by . This book was released on 1988 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear Regression Analysis written by George A. F. Seber and published by Wiley-Interscience. This book was released on 1977-01-27 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the theory and application of research techniques used in linar regression analysis. Dr. Seber gives a full discussion on the assumptions underlying regression models, and presents a variety of graphic and computational techniques for investigating these assumptions. His geometric approach enables the reader to deal with full rank and less than full rank models at the same time, and he varies the material by using the theory of generalized inverses to explain other approaches. Seber gives special attention to cases in straight-line and polynomial regression, analysis of variance and co-variance models associated with experimental designs in a theoretical framework. He also includes a number of topics which are usually omitted from most books, but are important in this area: optimal design, ridge estimators, two-phase regression, spline functions, and missing observations-with up-to-date sources of the literature available in these areas. Over 200 carefully selected problems, outline solutions, a reference bibliography, and appendices make this one of the most useful and informative books available on the subject of linear regression.

Download or read book Test for the Adequacy of Quasi likelihood Equation and Survival Analysis with Median Regression Models written by Sin-Ho Jung and published by . This book was released on 1992 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Regression with Linear Predictors written by Per Kragh Andersen and published by Springer. This book was released on 2010-07-15 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression,- gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear,logistic,proportional hazards,andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor:thatis,t-tests,one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2×2, 2×(k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model “held ?xed”.

Download or read book Multiple Imputation Approaches to Regression Analysis of Interval censored Failure Time Data written by Ling Chen and published by . This book was released on 2009 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation discusses regression analysis of interval-censored failure time data, which occur in many fields including demographical, epidemiological, financial, medical, and sociological studies (Sun, 2006). It consists of three parts. The first part considers regression analysis of current status data under the additive hazards model and in particular, we considered the situation where the observation times depend on covariates. The second part considers regression analysis of interval-censored failure time data under the additive hazards model and time-dependent covariates. The third part considers regression analysis of interval-censored failure time data under the linear transformation model. For these situations, we proposed a general semiparametric method based on multiple imputation for inference under the regression models. This multiple imputation converts the analysis of interval-censored failure time data to that of right-censored failure time data. A major advantage of the approach is its simplicity and it can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted and indicate that the approaches perform well for practical situations and are comparable to the existing methods. Real data applications are provided and model checking is discussed.

Download or read book Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by John Wiley & Sons. This book was released on 2015-06-29 with total page 679 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Download or read book Analysis of Multivariate Survival Data written by Philip Hougaard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.

Download or read book The Nonparametric Analysis of Interval censored Failure Time Data written by Ran Duan and published by . This book was released on 2013 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: By interval-censored failure time data, we mean that the failure time of interest is observed to belong to some windows or intervals, instead of being known exactly. One would get an interval-censored observation for a survival event if a subject has not experienced the event at one follow-up time but had experienced the event at the next follow-up time. Interval-censored data include right-censored data (Kalbfleisch and Prentice, 2002) as a special case. Nonparametric comparison of survival functions is one of the main tasks in failure time studies such as clinical trials. For interval-censored failure time data, a few nonparametric test procedures have been developed. However, due to the strict restrictions of existing nonparametric tests and practical demands, some new nonparametric tests need to be developed. This dissertation consists of four parts. In the first part, we propose a new class of test procedures whose asymptotic distributions are established under both null and alternative hypotheses, since all of the existing test procedures cannot be used if one intends to perform some power or sample size calculation under the alternative hypothesis. Some numerical results have been obtained from a simulation study for assessing the finite sample performance of the proposed test procedure. Also we applied the proposed method to a real data set arising from an AIDS clinical trial concerning the opportunistic infection cytomegalovirus (CMV). The second part of this dissertation will focus on the nonparametric test for intervalcensored data with unequal censoring. As we know, one common drawback or restriction of the nonparametric test procedures given in the literature is that they can only apply to situations where the observation processes follow the same distribution among different treatment groups. To remove the restriction, a test procedure is proposed, which takes into account the difference between the distributions of the censoring variables. Also the asymptotic distribution of the test statistics is developed by counting process and martingale theory. For the assessment of the performance of the procedure, a simulation study is conducted and suggested that it works well for practical situations. An illustrative example from a study aiming to investigate the HIV -1 infection risk among hemophilia patients is provided. The third part of this dissertation deals with the regression analysis of multivariate interval-censored data with informative censoring. Multivariate interval-censored failure time data often occur in the clinical trial that involves several related event times of interest and all the event times suffer interval censoring. Different types of models have been proposed for the regression analysis ( Zhang et al. (2008); Tong et al. (2008); Chen et al. (2009); Sun (2006)). However, most of these methods only deal with the situation where observation time is independent of the underlying survival time completely or given covariates. In this chapter, we discuss regression analysis of multivariate interval-censored data when the observation time may be related to the underlying survival time. An estimating equation based approach is proposed for regression coefficient estimate with the additive hazards frailty model and the asymptotic properties of the proposed estimates are established by using counting processes. A major advantage of the proposed method is that it does not involve estimation of any baseline hazard function. Simulation results suggest that the proposed method works well for practical situations. Finally, we will talk about the directions for future research. One is about the nonparametric test for interval-censored data with informative censoring. The other is about multiple generalized log-rank test for interval censored data.