Nonparametric Testing and Estimation with Interval-censored Data PDF Download

Author: Li Ding
Publisher:
ISBN:
Category :
Languages : en
Pages : 182

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Author: Ding-Geng (Din) Chen
Publisher: CRC Press
ISBN: 1466504250
Category : Mathematics
Languages : en
Pages : 435

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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.

Author: Ran Duan
Publisher:
ISBN:
Category : Clinical trials
Languages : en
Pages : 109

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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.

Author: Qiang Zhao
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 156

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Interval-censored failure time data commonly occur in follow-up studies. One of the main goals of such studies is to compare the distributions of the failure time among different treatment groups. By interval-censored data, we mean that the failure time of interest is not completely observed for some individuals. Instead, we only know that it belongs to a certain interval. This dissertation consists of three parts discussing the problem of comparing survival functions using nonparametric approaches based on two types of interval-censored data. First, Chapter 2 discusses the treatment comparison based on current status data, a special case of interval-censored data in which the failure time for each individual is either left- or right-censored. This part of research was motivated by comparing incidence rates of occult tumors, which is one of the main objectives of tumorigenicity experiments. For the problem, a common practice is to assume that tumors are either lethal or nonlethal (Hoel and Walburg, 1972; Sun, 1999), to fit incidence rate data to certain parametric or semiparametric models (Dewanji et al., 1993), or to treat tumors with intermediate, but known lethality (Lagakos and Louis, 1988). We propose a simple nonparametric test procedure for the incidence rate comparison that allows tumors to have intermediate and unknown lethality. This method also allows the distributions of death times to depend on treatments or doses. The proposed method is applied to a dataset arising from a tumorigenicity experiment. Chapters 3 and 4 consider the comparison of survival functions for case 2 interval-censored failure time data, which often occur in clinical trials and epidemiological studies. In Chapter 3, we generalize the most commonly used log-rank test (Mantel, 1966) for right-censored data to interval-censored data that may contain mixed types of observations: exactly observed, right-censored, left-censored, and interval-censored. Numerical studies are conducted to evaluate and compare the proposed test with existing methods, and the results indicate that the proposed test works well. We apply the method to a dataset arising from an AIDS cohort study, which motivated the study. A main drawback of most existing nonparametric test procedures for interval-censored data, including the one proposed in Chapter 3, is that they are ad-hoc and no asymptotic properties are established for the test statistics although they may be intuitive or simple. For those procedures, the derivation and calculation of the variance estimation are usually heuristical and complicated. In Chapter 4, we present a class of nonparametric tests for interval-censored data without exact observations and establish their asymptotics. These tests are generalizations of the log-rank test given in Peto and Peto (1972). Estimation of the variances of the test statistics is straightforward. The simulation results show that the proposed procedures perform satisfactorily. For illustration, we then apply the methods to a dataset from a cancer study.

Author: Jianguo Sun
Publisher: Springer
ISBN: 0387371192
Category : Mathematics
Languages : en
Pages : 310

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Book Description
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.

Author: Vilijandas Bagdonavicius
Publisher: John Wiley & Sons
ISBN: 1118602137
Category : Mathematics
Languages : en
Pages : 162

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This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.

Author: M.M. Desu
Publisher: CRC Press
ISBN: 1482285894
Category : Mathematics
Languages : en
Pages : 384

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Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the

Author: Jeremy Gorelick
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 122

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This thesis considers the problem of treatment comparisons when only interval-censored failure time data are available. This type of data occurs frequently in clinical trials and other follow-up studies. We study several nonparametric procedures developed previously and compare them under different situations. In particular, we study the situation where the difference between the groups occurs at an early or late time period. For this problem, we generalize the log-rank tests developed for interval-censored data in Zhao and Sun (2004) and the weighted log-rank test presented in Kalbfleisch (2002). Numerical studies are conducted to evaluate the proposed test and compare it with the unweighted log-rank test, which indicate that the proposed method works well. This thesis also considerers the problem of finding an appropriate sample size to achieve a desired power. We present a simple-to-use formula to find the sample size for a prespecified power and level of significance for the case of interval-censored data. Since many researchers use missing data techniques such as imputation along with right-censored methods to analyze interval-censored data, we also compare an imputed Kaplan-Meier Estimate of the survival function to Turnbull's Self Consistent Estimate. We present a large numerical study to show that these estimates often disagree at late time points when the mean survival time is large.

Author: Alain C. Vandal
Publisher:
ISBN:
Category : Nonparametric statistics
Languages : en
Pages : 216

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Author: 黃進龍
Publisher:
ISBN:
Category :
Languages : en
Pages : 124

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