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

ABSTRACT: 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.

SUBMITTER: Cho Y

PROVIDER: S-EPMC7954136 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Cho Youngjoo Y Ghosh Debashis D

Communications in statistics: theory and methods 20190715 1

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 sho ...[more]

PMID: 33716388

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