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Interval-censored Data (interval-censored + data)

Distribution by Scientific Domains
Mathematics and Statistics100%

Selected Abstracts

A Goodness-of-fit Test for the Marginal Cox Model for Correlated Interval-censored Failure Time Data

BIOMETRICAL JOURNAL, Issue 6 2006
Lianming Wang
Abstract The marginal Cox model approach is perhaps the most commonly used method in the analysis of correlated failure time data (Cai, 1999; Cai and Prentice, 1995; Lin, 1994; Wei, Lin and Weissfeld, 1989). It assumes that the marginal distributions for the correlated failure times can be described by the Cox model and leaves the dependence structure completely unspecified. This paper discusses the assessment of the marginal Cox model for correlated interval-censored data and a goodness-of-fit test is presented for the problem. The method is applied to a set of correlated interval-censored data arising from an AIDS clinical trial. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Survival Analysis in Clinical Trials: Past Developments and Future Directions

BIOMETRICS, Issue 4 2000
Thomas 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]

Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data

BIOMETRICS, Issue 2 2000
Daniel Rabinowitz
Summary. An easily implemented approach to fitting the proportional odds regression model to interval-censored data is presented. The approach is based on using conditional logistic regression routines in standard statistical packages. Using conditional logistic regression allows the practitioner to sidestep complications that attend estimation of the baseline odds ratio function. The approach is applicable both for interval-censored data in settings in which examinations continue regardless of whether the event of interest has occurred and for current status data. The methodology is illustrated through an application to data from an AIDS study of the effect of treatment with ZDV + ddC versus ZDV alone on 50% drop in CD4 cell count from baseline level. Simulations are presented to assess the accuracy of the procedure. [source]

A Multiple Imputation Approach to Cox Regression with Interval-Censored Data

BIOMETRICS, Issue 1 2000
Wei Pan
Summary. We propose a general semiparametric method based on multiple imputation for Cox regression with interval-censored data. The method consists of iterating the following two steps. First, from finite-interval-censored (but not right-censored) data, exact failure times are imputed using Tanner and Wei's poor man's or asymptotic normal data augmentation scheme based on the current estimates of the regression coefficient and the baseline survival curve. Second, a standard statistical procedure for right-censored data, such as the Cox partial likelihood method, is applied to imputed data to update the estimates. Through simulation, we demonstrate that the resulting estimate of the regression coefficient and its associated standard error provide a promising alternative to the nonparametric maximum likelihood estimate. Our proposal is easily implemented by taking advantage of existing computer programs for right,censored data. [source]