Download or read book Nonparametric and Semiparametric Censored Survival Analysis of Correlated Times to Event and Their Sequelae written by Adin-Cristian Andrei and published by . This book was released on 2005 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Interval Censored Time to Event Data written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2012-07-19 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.
Download or read book The Statistical Analysis of Interval censored Failure Time Data written by Jianguo Sun and published by Springer. This book was released on 2007-05-26 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.
Download or read book Survival Analysis Using S written by Mara Tableman and published by CRC Press. This book was released on 2003-07-28 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Methods for Two sample Comparisons from Censored Time to event Data written by Nubyra Ahmed and published by . This book was released on 2015 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the analysis of censored survival data, it is frequently of interest to determine the efficacy of a treatment or new method over a control or existing method. For this purpose, one may report estimates of the two survival functions or, more specifically, their difference, accompanied by simultaneous confidence bands (SCBs). Alternatively, or in addition, one may conduct hypothesis testing for the difference of the two survival functions. The first project exploits two bootstrap methods to develop new Wald-type SCBs for the difference of survival functions. The censored data bootstrap is employed to obtain nonparametric SCBs for the difference of two survival curves. Furthermore, a recently developed two-stage bootstrap is exploited to obtain semiparametric SCBs for the difference. The two-stage bootstrap combines the classical bootstrap with a model-based regeneration of censoring indicators. Simulation studies are presented to show that the new SCBs are superior to a currently existing one, in the sense of producing empirical coverage closer to the nominal level. The model-based approach produces tighter and, hence, more informative SCBs. Specifically, for censoring rates between 10% and 40%, the semiparametric SCBs provide a relative reduction in enclosed area amounting to between 2% and 7% over their nonparametric counterparts, with the increase in reduction being directly proportional to the censoring rate. In particular, the reduction is expected to be even higher for high censoring rates. The methods are illustrated using real data sets from cancer and other biomedical studies. The second project develops semiparametric SCBs for the difference using the method of empirical likelihood. Simulation studies are presented to show that the semiparametric approach is superior to the nonparametric counterpart, with the new SCBs producing empirical coverage closer to the nominal level. Further comparisons reveal that the semiparametric confidence bands are tighter and, hence, more informative. For censoring rates between 10% and 40%, the semiparametric confidence bands provide a relative reduction in enclosed area amounting to between 2% and 7% over their nonparametric bands, with increased reduction attained for higher censoring rates. The methods are illustrated using an University of Massachusetts AIDS data set. Finally, the third project develops two test procedures for the null hypothesis of no difference between the survival functions. The test statistics are based on the group-specific nonparametric or semiparametric survival function estimators. The censored data and two-stage bootstrap procedures are again deployed to obtain critical values for the testing. Numerical simulations show that the new test procedures outperform an existing one, in terms of producing the correct empirical significance level. Furthermore, power studies reinforce the superiority of the proposed method. A real example illustration is given to demonstrate the proposed method.
Download or read book Handbook of Survival Analysis written by John P. Klein and published by CRC Press. This book was released on 2016-04-19 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Download or read book Survival Analysis with Interval Censored Data written by Kris Bogaerts and published by CRC Press. This book was released on 2017-11-20 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features: -Provides an overview of frequentist as well as Bayesian methods. -Include a focus on practical aspects and applications. -Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website. The authors: Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University, Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat, KU Leuven. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval-censored data, misclassification issues, and clinical trials. He is the founding chair of the Statistical Modelling Society, past-president of the International Society for Clinical Biostatistics, and fellow of ISI and ASA.
Download or read book Survival Analysis in Medicine and Genetics written by Jialiang Li and published by CRC Press. This book was released on 2013-06-04 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples. Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.
Download or read book Nonparametric and Parametric Survival Analysis of Censored Data with Possible Violation of Method Assumptions written by Guolin Zhao and published by . This book was released on 2009 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Estimating survival functions has interested statisticians for numerous years. A survival function gives information on the probability of a time-to-event of interest. Research in the area of survival analysis has increased greatly over the last several decades because of its large usage in areas related to biostatistics and the pharmaceutical industry. Among the methods which estimate the survival function, several are widely used and available in popular statistical software programs. One purpose of this research is to compare the efficiency between competing estimators of the survival function. Results are given for simulations which use nonparametric and parametric estimation methods on censored data. The simulated data sets have right-, left-, or interval-censored time points. Comparisons are done on various types of data to see which survival function estimation methods are more suitable. We consider scenarios where distributional assumptions or censoring type assumptions are violated. Another goal of this research is to examine the effects of these incorrect assumptions."--Abstract from author supplied metadata.
Download or read book Nonparametric and Semiparametric Methods for Interval censored Failure Time Data written by Chao Zhu and published by . This book was released on 2006 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-censored failure time data commonly arise in follow-up studies such as clinical trials and epidemiology studies. For their analysis, what interests researcher most includes comparisons of survival functions for different groups and regression analysis. This dissertation, which consists of three parts, consider these problems on two types of interval-censored data by using nonparametric and semiparametric methods.
Download or read book Prognostic Factors and Predictions of Survival Data written by Duo Zhou and published by . This book was released on 2014 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survival outcome has been one of the major endpoints for clinical trials; it gives information on the probability of a time-to-event of interest. There has been increasing interest in survival analysis tools over the recent years, especially for high dimensional survival data. Common statistical approaches include nonparametric, semi-parametric and complete parametric analysis, several of which are widely used and readily available from major commercial software applications. However most of these approaches have limitations. Typical nonparametric approaches, such as the log-rank (or Cox-Mantel) test, are not concerned about model assumptions, but can only deal with a limited number of categorical predictors. Typical semi-parametric approaches, such as Cox proportional hazard model, depend very much on the model assumptions, such as linearity, interactions and proportionality; also these approaches can only deal with survival data when the number of predictors is less than the total number of events. Complete parametric models, such as accelerate failure time models, are similar to semi-parametric models except that they make further assumptions about the baseline hazard function. In this research paper, we studied several techniques for evaluating survival data, the typical Cox PH models including the generalized Cox linear model and the multivariate Cox regression models with nonlinear transformations, the nonparametric random survival forest approaches, penalized Cox regression models including lasso, ridge and elastic-net Cox regression models, derived-input Cox regression models including principal component Cox regression and partial least squares Cox regression models. These models were implemented and evaluated with one simulation study and one real world case study. The typical Cox models including the generalized Cox linear model and the multivariate Cox regression models with nonlinear transformations should always provide unbiased estimates, and the models are flexible for handling recurrent-event survival response; but they are incapable of making inferences for cases when there are more predictors than the actual number of events; and since they are semi-parametric approaches, model assumptions such as linearity, interaction and proportionality, should be carefully examined before the models were implemented. In this paper, a systematic procedure was proposed for examining the model assumptions, which should help to ensure the correct model was employed for the survival data. In terms of prediction performance, they are among the best approaches. In the paper, we also introduced nonparametric random survival forest approaches, log-rank based and conditional inference based random survival forest models, which have many advantages over the typical nonparametric, semi-parametric or parametric approaches. There are no concerns about model assumptions, and these methods can deal with many more predictors than typical survival models. In terms of prediction performance, these models are moderate and slightly worse than the typical Cox models. The penalized Cox regression models, on the other hand, should always give biased estimates; but they work quite well for cases when the number of factors is no less than the number of events. Of all penalized Cox models, the elastic-net Cox model works extremely well for correlated high dimensional data; the prediction performance is extremely good. However, they do not work for multiple event type of survival data. The principal component Cox regression model is a very useful tool for variable reduction with similar prediction performance as the typical Cox models. The model also has similar features as the typical Cox models; it can deal with recurrent event or interval censored survival data. But it also has many disadvantages, in cases when the number of components is no less than the total number of observations, the model may not be estimable; more importantly, analysis results from this model may be difficult to interpret. The partial least squares Cox regression model was developed; it shares some resemblance with principal component Cox regression model, the only difference is the construction of the components, instead of the building orthogonal components independent from the survival outcome, the model builds the PLS components to attain the strongest correlation with the survival outcome, otherwise it has similar features as the principal component Cox regression model. Additionally, the prediction performance of this model is unexpectedly very disappointing.
Download or read book The Analysis of Interval censored Survival Data From a Nonparametric Perspective to a Nonparametric Bayesian Approach written by and published by . This book was released on 1902 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This work concerns some problems in the area of survival analysis that arise in real clinical or epidemiological studies. In particular, we approach the problem of estimating the survival function based on interval-censored data or doubly-censored data. We will start defining these concepts and presenting a brief review of different methodologies to deal with this kind of censoring patterns. Survival analysis is the term used to describe the analysis of data that correspond to the time from a well defined origin time until the occurrence of some particular event of interest. This event need not necessarily be death, but could, for example, be the response to a treatment, remission from a disease, or the occurrence of a symptom.
Download or read book Survival Analysis written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.
Download or read book Semiparametric Analysis of Correlated Recurrent and Terminal Events written by Yining Ye and published by . This book was released on 2006 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many studies, survival data involve several types of failure. This is commonly referred as competing risk data. In other situations, failures or events can recur on the same subject. My research is focused on providing new methodologies to analyze data in which these complicated situations occur.
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.
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.
