Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation.

ABSTRACT: We consider the situation of estimating the marginal survival distribution from censored data subject to dependent censoring using auxiliary variables. We had previously developed a nonparametric multiple imputation approach. The method used two working proportional hazards (PH) models, one for the event times and the other for the censoring times, to define a nearest neighbor imputing risk set. This risk set was then used to impute failure times for censored observations. Here, we adapt the method to the situation where the event and censoring times follow accelerated failure time models and propose to use the Buckley-James estimator as the two working models. Besides studying the performances of the proposed method, we also compare the proposed method with two popular methods for handlin

SUBMITTER: Hsu CH 

PROVIDER: S-EPMC5863093 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature