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.

中文翻译：

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来自复杂调查的事件时间数据的模型辅助估计器

我们为复杂的调查数据开发了模型辅助的估计器，用于在指定时间t之前经历过某些事件的人口比例。新估算器的理论使用事件发生时间模型作为基础框架，但同时具有良好的基于​​模型和基于设计的属性。在模拟中将估计量与传统的调查估计方法进行比较，还将其应用于护士健康研究。与传统的替代方法相比，新的估算器利用了预测事件的协变量并减少了标准误差。