The Statistical Analysis of Interval censored Failure Time Data

The Statistical Analysis of Interval censored Failure Time Data

This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data.

Author: Jianguo Sun

Publisher: Springer

ISBN: 0387329056

Category: Mathematics

Page: 304

View: 861

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.
Categories: Mathematics

Statistical Analysis of Interval censored Failure Time Data

Statistical Analysis of Interval censored Failure Time Data

In this thesis, we will examine the statistical methods used in survival analysis applied to interval-censored failure time data.

Author: Alicia Worrall

Publisher:

ISBN: 133907026X

Category: Clinical trials

Page: 75

View: 205

In this thesis, we will examine the statistical methods used in survival analysis applied to interval-censored failure time data. Interval-censored data is not widely used due to the fact that it is more difficult to work with. However, the same methods commonly used for random- censoring can be applied to interval-censoring as well. This includes finding the basic quantities, survival curves, regression analysis, Bayesian regression analysis and a comparison between interval-censored data and random-censored data.
Categories: Clinical trials

Statistical Analysis of Multivariate Interval censored Failure Time Data

Statistical Analysis of Multivariate Interval censored Failure Time Data

A voluminous literature on right-censored failure time data has been developed in the past 30 years.

Author: Man-Hua Chen

Publisher:

ISBN: OCLC:312781412

Category: Electronic dissertations

Page:

View: 369

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.
Categories: Electronic dissertations

Regression Analysis of Interval censored Failure Time Data with Non Proportional Hazards Models

Regression Analysis of Interval censored Failure Time Data with Non Proportional Hazards Models

The resulting estimators of regression parameters are shown to be consistent and asymptotically normal. Several numerical results suggest that the proposed method works well in practical situations.

Author: Han Zhang (Graduate of University of Missouri)

Publisher:

ISBN: OCLC:1258038880

Category:

Page: 135

View: 377

Interval-censored failure time data arises when the failure time of interest is known only to lie within an interval or window instead of being observed exactly. Many clinical trials and longitudinal studies may generate interval-censored data. One common area that often produces such data is medical or health studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of interval-censored data is the so-called current status data when each study subject is observed only once for the status of the event of interest. That is, instead of observing the survival endpoint directly, we will only know the observation time and whether or not the event of interest has occurred by that time. Such data may occur in many fields as cross-sectional studies and tumorigenicity experiments. Sometimes we also refer current status data as case I interval-censored data and the general case as case II interval-censored data. Recently the semi-parametric statistical analysis of both case I and case II intervalcensored failure time data has attracted a great deal of attention. Many procedures have been proposed for their regression analysis under various models. We will describe the structure of interval-censored data in Chapter 1 and provides two specific examples. Also some special situations like informative censoring and failure time data with missing covariates are discussed. Besides, a brief review of the literature on some important topics, including nonparametric estimation and regression analysis are performed. However, there are still a number of problems that remain unsolved or lack approaches that are simpler, more efficient and could apply to more general situations compared to the existing ones. For regression analysis of interval-censored data, many approaches have been proposed and more specifically most of them are developed for the widely used proportional hazards model. The research in this dissertation focuses on the statistical analysis on non-proportional hazards models. In Chapter 2 we will discuss the regression analysis of interval-censored failure time data with possibly crossing hazards. For the problem of crossing hazards, people assume that the hazard functions with two samples considered may cross each other where most of the existing approaches cannot deal with such situation. Many authors has provided some efficient methods on right-censored failure time data, but little articles could be found on interval-censored data. By applying the short-term and long-term hazard ratio model, we develop a spline-based maximum likelihood estimation procedure to deal with this specific situation. In the method, a splined-based sieve estimation are used to approximate the underlying unknown function. The proposed estimators are shown to be strongly consistent and the asymptotic normality of the estimators of regression parameters are also shown to be true. In addition, we also provided a Cramer-Raw type of criterion to do the model validation. Simulation study was conducted for the assessment of the finite sample properties of the presented procedure and suggests that the method seems to work well for practical situations. Also an illustrative example using a data set from a tumor study is provided. As we discussed in Chapter 1, several semi-parametric and non-parametric methods have been proposed for the analysis of current status data. However, most of them only deal with the situation where observation time is independent of the underlying survival time. In Chapter 3, we consider regression analysis of current status data with informative observation times in additive hazards model. In many studies, the observation time may be correlated to the underlying failure time of interest, which is often referred to as informative censoring. Several authors have discussed the problem and in particular, an estimating equation-based approach for fitting current status data to additive hazards model has been proposed previously when informative censoring occurs. However, it is well known that such procedure may not be efficient and to address this, we propose a sieve maximum likelihood procedure. In particular, an EM algorithm is developed and the resulting estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study was conducted for the assessment of the finite sample properties of the presented procedure and suggests that it seems to work well for practical situations. An application to a tumorigenicity experiment is also provided. In Chapter 4, we considered another special case under the additive hazards model, case II interval-censored data with possibly missing covariates. In many areas like demographical, epidemiological, medical and sociological studies, a number of nonparametric or semi-parametric methods have been developed for interval-censored data when the covariates are complete. However, it is well-known that in reality some covariates may suffer missingness due to various reasons, data with missing covariates could be very common in these areas. In the case of missing covariates, a naive method is clearly the complete-case analysis, which deletes the cases or subjects with missing covariates. However, it's apparent that such analysis could result in loss of efficiency and furthermore may lead to biased estimation. To address this, we propose the inverse probability weighted method and reweighting approach to estimate the regression parameters under the additive hazards model when some of the covariates are missing at random. The resulting estimators of regression parameters are shown to be consistent and asymptotically normal. Several numerical results suggest that the proposed method works well in practical situations. Also an application to a health survey is provided. Several directions for future research are discussed in Chapter 5.
Categories:

The Statistical Analysis of Interval censored Failure Time Data

The Statistical Analysis of Interval censored Failure Time Data

In the past 20 years or so, a voluminous literature on the statistical analysis of interval-censored failure time data has appeared.

Author: Jianguo Sun

Publisher: Springer

ISBN: 0387371192

Category: Mathematics

Page: 304

View: 949

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.
Categories: Mathematics

Statistical Analysis of Multivariate Interval censored Failure Time Data

Statistical Analysis of Multivariate Interval censored Failure Time Data

Interval-censored failure time data commonly arise in clinical trials and medical studies.

Author: Lianming Wang

Publisher:

ISBN: OCLC:123911987

Category: Electronic dissertations

Page:

View: 525

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.
Categories: Electronic dissertations

Statistical Analysis of Bivariate Interval censored Failure Time Data

Statistical Analysis of Bivariate Interval censored Failure Time Data

This dissertation deals with various issues in the statistical analysis of bivariate interval-censored failure time data, including regression analysis, model selection and estimation of the association between failure times.

Author: Qingning Zhou

Publisher:

ISBN: OCLC:965801586

Category:

Page: 135

View: 454

This dissertation deals with various issues in the statistical analysis of bivariate interval-censored failure time data, including regression analysis, model selection and estimation of the association between failure times. In particular, it includes three projects. The first project discusses regression analysis of bivariate current status data under the marginal proportional hazards model. For the problem, by using Bernstein polynomials and an unspecified copula model, we develop a sieve maximum likelihood estimation approach that applies to very general situations. In particular, it allows one to estimate the underlying copula model and can be easily implemented. The strong consistency, asymptotic normality and efficiency of the estimators of regression parameters are established. In the second project, we consider regression analysis of bivariate case II interval-censored data. For this problem, we present a class of semiparametric transformation models which is very flexible and in particular includes the commonly used proportional hazards model as a special case. Also, for inference, we develop a sieve maximum likelihood approach based on Bernstein polynomials. The strong consistency, asymptotic normality and efficiency of the resulting estimators of the regression parameters are established. In the third project, we consider the class of semiparametric copula-based models, in which multivariate survival functions are characterized by parametric copulas and nonparametric marginal survival functions. One important issue in applying this class of models to a given data set is how to choose an appropriate parametric copula. We propose two model selection procedures for Archimedean copulas with bivariate interval-censored data. The first procedure is based on a comparison of the nonparametric and model-based estimators of the probability integral transformation K, while the second procedure is based on a pseudo-likelihood function.
Categories:

Semi parametric Regression Analysis of Interval censored Failure Time Data

Semi parametric Regression Analysis of Interval censored Failure Time Data

To assess the finite sample performance of the proposed method, an extensive simulation study is conducted and indicates that the method works well in practical situations.

Author: Ling Ma

Publisher:

ISBN: OCLC:906965137

Category: Electronic dissertations

Page:

View: 596

By interval-censored data, we mean that the failure time of interest is known only to lie within an interval instead of being observed exactly. Many clinical trials and longitudinal studies may generate interval-censored data. One common example occurs in medical or health studies that entail periodic follow-ups. An important special case of interval-censored data is the so called current status data when each subject is observed only once for the status of the occurrence of the event of interest. That is, instead of observing the survival endpoint directly, we only know the observation time and whether or not the event of interest has occurred at that time. Such data may occur in many fields, for example, cross-sectional studies and tumorigenicity experiments. Sometimes we also refer current status data to as case I interval-censored data and the general case as case II interval-censored data. In the following, for simplicity, we will refer current status data and interval-censored data to case I and case II interval-censored data, respectively. The statistical analysis of both case I and case II interval-censored failure time data has recently attracted a great deal of attention and especially, many procedures have been proposed for their regression analysis under various models. However, due to the strict restrictions of existing regression analysis procedures and practical demands, new methodologies for regression analysis need to be developed. For regression analysis of interval-censored data, many approaches have been proposed and for most of them, the inference is carried out based on the asymptotic normality. It's well known that the symmetric property implied by the normal distribution may not be appropriate sometimes and could underestimate the variance of estimated parameters. In the first part of this dissertation, we adopt the linear transformation models for regression analysis of interval-censored data and propose an empirical likelihood-based procedure to address the underestimating problem from using symmetric property implied by the normal distribution of the parameter estimates. Simulation and analysis of a real data set are conducted to assess the performance of the procedure. The second part of this dissertation discusses regression analysis of current status data under additive hazards models. In this part, we focus on the situation when some covariates could be missing or cannot be measured exactly due to various reasons. Furthermore, for missing covariates, there may exist some related information such as auxiliary covariates (Zhou and Pepe, 1995). We propose an estimated partial likelihood approach for estimation of regression parameters that make use of the available auxiliary information. To assess the finite sample performance of the proposed method, an extensive simulation study is conducted and indicates that the method works well in practical situations. Several semi-parametric and non-parametric methods have been proposed for the analysis of current status data. However, most of these methods deal only with the situation where observation time is independent of the underlying survival time completely or given covariates. The third part of this dissertation discusses regression analysis of current status data when the observation time may be related to survival time. The correlation between observation time and survival time and the covariate effects are described by a copula model and the proportional hazards model, respectively. For estimation, a sieve maximum likelihood procedure with the use of monotone I-spline functions is proposed and the proposed method is examined through a simulation study and illustrated with a real data set. In the fourth part of this dissertation, we discuss the regression analysis of interval- censored data where the censoring mechanism could be related to the failure time. We consider a situation where the failure time depend on the censoring mechanism only through the length of the observed interval. The copula model and monotone I-splines are used and the asymptotic properties of the resulting estimates are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An extensive simulation study and an illustrative example is provided. Finally, we will talk about the directions for future research. One topic related the fourth part of this dissertation for future research could be to allow the failure time to depend on both the lower and upper bounds of the observation interval. Another possible future research topic could be to consider a cure rate model for interval-censored data with informative censoring.
Categories: Electronic dissertations

Semiparametric Analysis of Failure Time Data with Complex Structures

Semiparametric Analysis of Failure Time Data with Complex Structures

A simulation study is conducted and suggests that the proposed approach works well in practical situations. Also an illustrative example is provided.

Author: Yeqian Liu

Publisher:

ISBN: OCLC:1028577582

Category:

Page: 119

View: 106

Failure time data arise in many fields including biomedical studies and industrial life testing. Right-censored failure time data are often observed from a cohort of prevalent cases that are subject to length-biased sampling, which are termed as length-biased and right-censored data. Interval-censored failure time data arise when the failure time of interest in a survival study is not exactly observed but known only to fall within some interval or window. One area that often produces such data is medical studies with periodic follow-ups, in which the medical condition of interest such as the onset of a disease is only known to occur between two adjacent examination times. An important special case of interval-censored data is current status data which arise when each study subject is observed only once and the only information available is whether the failure event of interest has occurred or not by the observation time. Sometimes we also refer current status data as case I interval-censored data and the general case as case II interval-censored data. Semiparametric regression analysis of both right-censored and interval-censored failure time data has recently attracted a great deal of attention. Many procedures have been proposed for their regression analysis under various models. However, in many settings, the population include a cured (nonsusceptible) subpopulation, where only individuals in the susceptible subpopulation will go on to experience the event. Since classical survival models implicitly assume that all individuals will eventually experience the event of interest, they cannot be used in such contexts. They would in fact lead to incorrect results such as, among others, an overestimation of the survival of the non-cured subjects. The research in this dissertation focuses on the statistical analysis for right-censored data with length-biased sampling, interval-censored data with a cured subgroup in the presence of potential dependent censoring and measurement errors. Chapter 1 describes specific examples of right-censored and interval-censored failure time data and reviews the literature on some important topics, including nonparametric and semiparametric estimation, regression analysis in the presence of length-biased sampling and a cured subgroup respectively. Chapter 2 discusses regression analysis of length-biased and right-censored data with with partially linear varying effects. For this problem, we consider quantile regression analysis of right-censored and length-biased data and present a semiparametric varying coefficient partially linear model. For estimation of regression parameters, a three-stage procedure that makes use of the inverse probability weighted technique is developed, and the asymptotic properties of the resulting estimators are established. In addition, the approach allows the dependence of the censoring variable on covariates, while most of the existing methods assume the independence between censoring variables and covariates. A simulation study is conducted and suggests that the proposed approach works well in practical situations. Also an illustrative example is provided. Chapter 3 considers regression analysis of current status data in the presence of a cured subgroup and dependent censoring. For the problem, we develop a sieve maximum likelihood estimation approach with the use of latent variables and Bernstein polynomials. For the determination of the proposed estimators, an EM algorithm and the asymptotic properties of the estimators are established. An extensive simulation study conducted to asses the finite sample performance of the method indicates that it performs well for practical situations. An illustrative example using a data set from a tumor toxicological study is provided. Chapter 4 considers regression analysis of interval-censored data in the presence of a cured subgroup and the case where one or more explanatory variables in the model are subject to measurement errors. These errors should be taken into account in the estimation of the model, to avoid biased estimations. A general approach that exists in the literature is the SIMEX algorithm, a method based on simulations which allows one to estimate the effect of measurement error on the bias of the estimators and to reduce this bias. We extend the SIMEX approach to the mixture cure model with interval-censored data. Comprehensive simulations study as well as a real data application are provided. Several directions for future research are discussed in Chapter 5.
Categories:

Survival Analysis with Interval Censored Data

Survival Analysis with Interval Censored Data

The Annals of Statistics , 25 (4), 1371– 1470. Sturtz, S., Ligges, U. & Gelman, ... The statistical analysis of interval-censored failure time data.

Author: Kris Bogaerts

Publisher: CRC Press

ISBN: 9781351643054

Category: Mathematics

Page: 584

View: 557

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.
Categories: Mathematics

Statistical Analysis of Interval censored and Truncated Survival Data

Statistical Analysis of Interval censored and Truncated Survival Data

Data from clinical trials and epidemiological studies are often incomplete due to interval censoring and truncation.

Author: Hee-Jeong Lim

Publisher:

ISBN: OCLC:49632193

Category: Survival analysis (Biometry)

Page: 232

View: 688

Data from clinical trials and epidemiological studies are often incomplete due to interval censoring and truncation. In this thesis, we will discuss the statistical analysis of survival data with interval-censoring and truncation. First, we consider the problem of comparing two failure time distributions based on interval-censored data. We propose three classes of nonparametric test procedures, which include most existing methods as special cases. To evaluate and compare the proposed and existing tests and to draw a guideline for selecting an appropriate test for a given situation, an extensive simulation study is conducted. Secondly, we consider the problem of estimating a survival function when there exists a change point. To obtain the maximum likelihood estimator of a survival function in this case, an EM algorithm is developed when the survival function is completely unknown before the change point and known up to a vector of unknown parameters after the change point. We evaluate the performance of the proposed algorithm and illustrate it using a set of survival data arising from an AIDS study. Thirdly, we consider a regression analysis of survival data with interval-censored covariates. To estimate regression parameters, methods based on estimating equations are developed. An extensive simulation study is performed to evaluate the proposed method.
Categories: Survival analysis (Biometry)

The Nonparametric Analysis of Interval censored Failure Time Data

The Nonparametric Analysis of Interval censored Failure Time Data

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.

Author: Ran Duan

Publisher:

ISBN: OCLC:890468993

Category: Clinical trials

Page: 109

View: 894

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.
Categories: Clinical trials

Encyclopedia of Biopharmaceutical Statistics Four Volume Set

Encyclopedia of Biopharmaceutical Statistics   Four Volume Set

CONCLUSION This entry provided a relatively comprehensive overview of the issues and approaches in the analysis of interval-censored failure time data .

Author: Shein-Chung Chow

Publisher: CRC Press

ISBN: 9781351110266

Category: Medical

Page: 2780

View: 552

Since the publication of the first edition in 2000, there has been an explosive growth of literature in biopharmaceutical research and development of new medicines. This encyclopedia (1) provides a comprehensive and unified presentation of designs and analyses used at different stages of the drug development process, (2) gives a well-balanced summary of current regulatory requirements, and (3) describes recently developed statistical methods in the pharmaceutical sciences. Features of the Fourth Edition: 1. 78 new and revised entries have been added for a total of 308 chapters and a fourth volume has been added to encompass the increased number of chapters. 2. Revised and updated entries reflect changes and recent developments in regulatory requirements for the drug review/approval process and statistical designs and methodologies. 3. Additional topics include multiple-stage adaptive trial design in clinical research, translational medicine, design and analysis of biosimilar drug development, big data analytics, and real world evidence for clinical research and development. 4. A table of contents organized by stages of biopharmaceutical development provides easy access to relevant topics. About the Editor: Shein-Chung Chow, Ph.D. is currently an Associate Director, Office of Biostatistics, U.S. Food and Drug Administration (FDA). Dr. Chow is an Adjunct Professor at Duke University School of Medicine, as well as Adjunct Professor at Duke-NUS, Singapore and North Carolina State University. Dr. Chow is the Editor-in-Chief of the Journal of Biopharmaceutical Statistics and the Chapman & Hall/CRC Biostatistics Book Series and the author of 28 books and over 300 methodology papers. He was elected Fellow of the American Statistical Association in 1995.
Categories: Medical

Statistical Analysis of Failure Time Data with Missing Information

Statistical Analysis of Failure Time Data with Missing Information

Failure time data arise in many fields and can involve different types of censoring structures and missing information.

Author: Ping Chen

Publisher:

ISBN: OCLC:514118371

Category: Electronic dissertations

Page: 72

View: 978

Failure time data arise in many fields and can involve different types of censoring structures and missing information. We consider three cases: right-censored data with missing censoring indicators, clustered current status data, and clustered interval-censored data. Chapter 2 discusses regression analysis of right-censored failure time data with missing censoring indicators and presents an efficient estimation procedure based on the EM algorithm. The simulation study performed indicates that the proposed methodology performs well for practical situations. An illustrative example from a breast cancer clinical trial is provided. Chapter 3 discusses regression analysis of clustered current status data. For inference, a Cox frailty model and a two-step EM algorithm are presented. A simulation study was conducted for the evaluation of the proposed methodology and indicates that the approach performs well for practical situations. An illustrative example from a tumorigenicity experiment is provided. Chapter 4 generalizes the study of Chapter 3 to clustered interval-censored data. Chapter 5 discusses some directions for future research.
Categories: Electronic dissertations

Interval Censored Time to Event Data

Interval Censored Time to Event Data

A semiparametric probit model for case 2 interval-censored failure time data. ... The Statistical Analysis of Interval-Censored Failure Time Data.

Author: Ding-Geng (Din) Chen

Publisher: CRC Press

ISBN: 9781466504257

Category: Mathematics

Page: 434

View: 435

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.
Categories: Mathematics

Clinical Trial Biostatistics and Biopharmaceutical Applications

Clinical Trial Biostatistics and Biopharmaceutical Applications

Statistical analysis of doubly interval-censored failure time data. In Balakrishnan, N. and Rao, C.R. (Eds.) Handbook of Statistics: Advances in Survival ...

Author: Walter R. Young

Publisher: CRC Press

ISBN: 9781482212198

Category: Mathematics

Page: 580

View: 442

Since 1945, "The Annual Deming Conference on Applied Statistics" has been an important event in the statistics profession. In Clinical Trial Biostatistics and Biopharmaceutical Applications, prominent speakers from past Deming conferences present novel biostatistical methodologies in clinical trials as well as up-to-date biostatistical applications
Categories: Mathematics

Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics

Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics

The Statistical Analysis of Interval-Censored Failure Time Data. New York: Springer. D. M. Finkelstein, and R. A. Wolfe (1985). A semiparametric model for ...

Author: Jianchang Lin

Publisher: Springer

ISBN: 9783319425689

Category: Medical

Page: 359

View: 170

The papers in this volume represent a broad, applied swath of advanced contributions to the 2015 ICSA/Graybill Applied Statistics Symposium of the International Chinese Statistical Association, held at Colorado State University in Fort Collins. The contributions cover topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. Each papers was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe.
Categories: Medical

The Statistical Analysis of Failure Time Data

The Statistical Analysis of Failure Time Data

Addressing graduate students, practitioners, and researchers, Jack Kalbfleisch and Ross Prentice update their classic text with these and other current developments in the second edition of The Statistical Analysis of Failure Time Data.

Author: John D. Kalbfleisch

Publisher: John Wiley & Sons

ISBN: 9781118031230

Category: Mathematics

Page: 462

View: 740

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.
Categories: Mathematics

Handbook of Survival Analysis

Handbook of Survival Analysis

Sun, J. (2002), Statistical analysis of doubly interval-censored failure time data, Handbook of Statistics: Survival Analysis, Balakrishnan, N. and Rao, ...

Author: John P. Klein

Publisher: CRC Press

ISBN: 9781466555679

Category: Mathematics

Page: 656

View: 231

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
Categories: Mathematics

Encyclopedia of Quantitative Risk Analysis and Assessment

Encyclopedia of Quantitative Risk Analysis and Assessment

The Statistical Analysis of Failure Time Data, 2nd Edition, John Wiley & Sons, ... A proportional hazards model for interval-censored failure time data, ...

Author:

Publisher: John Wiley & Sons

ISBN: 9780470035498

Category: Mathematics

Page: 2176

View: 267

Leading the way in this field, the Encyclopedia of Quantitative Risk Analysis and Assessment is the first publication to offer a modern, comprehensive and in-depth resource to the huge variety of disciplines involved. A truly international work, its coverage ranges across risk issues pertinent to life scientists, engineers, policy makers, healthcare professionals, the finance industry, the military and practising statisticians. Drawing on the expertise of world-renowned authors and editors in this field this title provides up-to-date material on drug safety, investment theory, public policy applications, transportation safety, public perception of risk, epidemiological risk, national defence and security, critical infrastructure, and program management. This major publication is easily accessible for all those involved in the field of risk assessment and analysis. For ease-of-use it is available in print and online.
Categories: Mathematics