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

页码：4351 / 4371

页数：21

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