Model-assisted estimators for time-to-event data from complex surveys

被引：1
|
作者：
Reist, Benjamin M. [1 ]
Valliant, Richard [2 ]
机构：
[1] NASA, Off CIO, Washington, DC 20546 USA
[2] Univ Michigan, Survey Res Ctr, Ann Arbor, MI 48109 USA
来源：
STATISTICS IN MEDICINE | 2020年 / 39卷 / 29期
基金：
美国国家卫生研究院;
关键词：
doubly robust; general difference estimator; model calibrated estimator; time-to-failure model; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS; THRESHOLD REGRESSION; INFERENCE;
D O I：
10.1002/sim.8728
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We develop model-assisted estimators for complex survey data for the proportion of a population that experienced some event by a specified timet. Theory for the new estimators uses time-to-event models as the underlying framework but have both good model-based and design-based properties. The estimators are compared in a simulation to traditional survey estimation methods and are also applied to a study of nurses' health. The new estimators take advantage of covariates predictive of the event and reduce standard errors compared to conventional alternatives.
引用
收藏
页码：4351 / 4371
页数：21
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