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Dependent Censoring (dependent + censoring)
Selected AbstractsAnalysis of Times to Repeated Events in Two-Arm Randomized Trials with Noncompliance and Dependent CensoringBIOMETRICS, Issue 4 2004Shigeyuki Matsui Summary This article develops randomization-based methods for times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Structural accelerated failure time models are assumed to capture causal effects on repeated event times and dependent censoring time, but the dependence structure among repeated event times and dependent censoring time is unspecified. Artificial censoring techniques to accommodate nonrandom noncompliance and dependent censoring are proposed. Estimation of the acceleration parameters are based on rank-based estimating functions. A simulation study is conducted to evaluate the performance of the developed methods. An illustration of the methods using data from an acute myeloid leukemia trial is provided. [source] Regression analysis based on semicompeting risks dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2008Jin-Jian Hsieh Summary., Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible regression model is imposed on the progression time which is of major interest. Model checking procedures are also proposed to help to choose a best-fitted model. Under a two-sample setting, Lin and co-workers proposed a competing method which requires an additional marginal assumption on the terminal event and implicitly assumes that the dependence structures in the two groups are the same. Using simulations, we compare the two approaches on the basis of their finite sample performances and robustness properties under model misspecification. The method proposed is applied to a bone marrow transplant data set. [source] Mixture cure survival models with dependent censoringJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2007Yi Li Summary., The paper is motivated by cure detection among the prostate cancer patients in the National Institutes of Health surveillance epidemiology and end results programme, wherein the main end point (e.g. deaths from prostate cancer) and the censoring causes (e.g. deaths from heart diseases) may be dependent. Although many researchers have studied the mixture survival model to analyse survival data with non-negligible cure fractions, none has studied the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model that allows for dependent censoring. We derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time by using a class of Archimedean copula models. Within this framework, we consider the parameter estimation, the cure detection and the two-sample comparison of latency distributions in the presence of dependent censoring when a proportion of patients is deemed cured. Large sample results by using martingale theory are obtained. We examine the finite sample performance of the proposed methods via simulation and apply them to analyse the surveillance epidemiology and end results prostate cancer data. [source] Analysis of Times to Repeated Events in Two-Arm Randomized Trials with Noncompliance and Dependent CensoringBIOMETRICS, Issue 4 2004Shigeyuki Matsui Summary This article develops randomization-based methods for times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Structural accelerated failure time models are assumed to capture causal effects on repeated event times and dependent censoring time, but the dependence structure among repeated event times and dependent censoring time is unspecified. Artificial censoring techniques to accommodate nonrandom noncompliance and dependent censoring are proposed. Estimation of the acceleration parameters are based on rank-based estimating functions. A simulation study is conducted to evaluate the performance of the developed methods. An illustration of the methods using data from an acute myeloid leukemia trial is provided. [source] Survival Analysis in Clinical Trials: Past Developments and Future DirectionsBIOMETRICS, Issue 4 2000Thomas R. Fleming Summary. The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association53, 457,481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report50, 163,170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B34, 187,220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods. [source] | ||||||||||