Semiparametric analysis of recurrent events: artificial censoring, truncation, pairwise estimation and inference

Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring

Dependent censoring is common in many medical studies, especially when there are multiple occurrences of the event of interest. Ghosh and Lin (2003) and Hsieh, Ding and Wang (2011) proposed estimation procedures using an artificial censoring technique. However, if covariates are not bounded, then these methods can cause excessive artificial censoring. In this paper, we propose estimation procedures for the treatment effect based on a novel application of propensity scores. Simulation studies show that the proposed method provides good finite-sample properties. The techniques are illustrated with an application to an HIV dataset.

Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model

In many medical studies, estimation of the association between treatment and outcome of interest is often of primary scientific interest. Standard methods for its evaluation in survival analysis typically require the assumption of independent censoring. This assumption might be invalid in many medical studies, where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes. This data structure is called "semicompeting risks data," for which many authors have proposed an artificial censoring technique. However, confounders with large variability may lead to excessive artificial censoring, which subsequently results in numerically unstable estimation. In this paper, we propose a strategy for weighted estimation of the associations in the accelerated failure time model. Weights are based on propensity score modeling of the treatment conditional on confounder variables. This novel application of propensity scores avoids excess artificial censoring caused by the confounders and simplifies computation. Monte Carlo simulation studies and application to AIDS and cancer research are used to illustrate the methodology.

Weighted estimation of the accelerated failure time model in the presence of dependent censoring

Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring technique in this data structure. However, neither of these estimators is better than another with respect to efficiency (standard error). In this paper, we propose a new weighted estimator for the accelerated failure time (AFT) model under dependent censoring. One of the advantages in our approach is that these weights are optimal among all the linear combinations of the previously mentioned two estimators. To calculate these weights, a novel resampling-based scheme is employed. Attendant asymptotic statistical results for the estimator are established. In addition, simulation studies, as well as an application to real data, show the gains in efficiency for our estimator.