Recurrent events data are commonly seen in longitudinal follow-up studies. Dependent censoring often occurs due to death or exclusion from the study related to the disease process. In this article, we assume flexible marginal regression models on the recurrence process and the dependent censoring time without specifying their dependence structure. The proposed model generalizes the approach by ...

We propose a beta product confidence procedure (BPCP) that is a non-parametric confidence procedure for the survival curve at a fixed time for right-censored data assuming independent censoring. In such situations, the Kaplan-Meier estimator is typically used with an asymptotic confidence interval (CI) that can have coverage problems when the number of observed failures is not large, and/or whe...

the 5-year survival rate is a good prognostic indicator for patients with gastric cancer that is usually estimated based on kaplan-meier. in situations where censored observations are too many, this method produces biased estimations. this study aimed to compare estimations of kaplan-meier and weighted kaplan-meier as an alternative method to deal with the problem of heavy-censoring. data from ...

Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administr...

In this paper we develop a locally eecient one-step estimator of a multivariate survival function and functionals thereof when all time-variables are subject to right-censoring by a common censoring variable. In particular, this estimator can be applied to estimate the gap time distributions corresponding with an ordered sequence of events. This estimator can incorporate a time-dependent covari...

This paper provides an overview of nonparametric predictive inference for comparison of multiple groups of data including right-censored observations. Different right-censoring schemes discussed are early termination of an experiment, progressive censoring and competing risks. Theoretical results are briefly stated, detailed justifications are presented elsewhere. The methods are illustrated an...

BACKGROUND Recent studies have suggested that statins possess diverse immune modulatory and anti-inflammatory properties. As statins might attenuate inflammation, statin therapy has been hypothesized to reduce mortality in HIV-infected individuals. We therefore used a Danish nationwide cohort of HIV-infected individuals to estimate the impact of statin use on mortality before and after a diagno...

We show how to combine censoring technique for Markov chain and strong stochastic comparison to obtain bounds on rewards and the first passage time. We present the main ideas of the method, the algorithms and their proofs. We obtain a substantial reduction of the state space due to the censoring technique. We also present some numerical results to illustrate the effectiveness of the method.

We consider the problem of estimation of the parameters of the Marshall-Olkin Bivariate Weibull distribution in the presence of random censoring. Since the maximum likelihood estimators of the parameters can not be expressed in a closed form, we suggest an EM algorithm to compute the same. Extensive simulations are done to conclude that the estimators perform efficiently under random censoring.