Inference in randomized studies with informative censoring and discrete time-to-event endpoints

被引：36
|
作者：
Scharfstein, D [1 ]
Robins, JM
Eddings, W
Rotnitzky, A
机构：
[1] Johns Hopkins Sch Hyg & Publ Hlth, Dept Biostat, Baltimore, MD 21025 USA
[2] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
[3] Harvard Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
来源：
BIOMETRICS | 2001年 / 57卷 / 02期
关键词：
coarsening at random; competing risks; curse of dimensionality; inverse probability of censoring weighted estimation; Kaplan-Meier estimator; sequential ignorability of censoring;
D O I：
10.1111/j.0006-341X.2001.00404.x
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this article. we present a method for estimating and comparing the treatment-specific distributions of a discrete time-to-event variable from right-censored data. Our method allows for (1) adjustment for informative censoring due to measured prognostic factors for time to event and censoring and (2) quantification of the sensitivity of the inference to residual dependence between time to event and censoring due to unmeasured factors. We develop our approach in the context of a randomized trial for the treatment of chronic schizophrenia. We perform a simulation study to assess the practical performance of our methodology.
引用
收藏
页码：404 / 413
页数：10
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