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Model‐assisted estimators for time‐to‐event data from complex surveys
Author(s) -
Reist Benjamin M.,
Valliant Richard
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8728
Subject(s) - estimator , covariate , event data , event (particle physics) , computer science , statistics , estimation , econometrics , population , mathematics , medicine , engineering , physics , systems engineering , quantum mechanics , environmental health
We develop model‐assisted estimators for complex survey data for the proportion of a population that experienced some event by a specified time t . 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.