An Estimated Likelihood Method for Continuous Outcome Regression Models With Outcome-Dependent Sampling
结果依赖抽样下连续结果回归模型的估计似然方法AI 解读 ↓
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结果依赖抽样下连续结果回归模型的估计似然方法
- 研究问题
- 当全体研究对象的结果可观测、昂贵的暴露测量仅在按结果抽取的子样本中获得时，如何避免设计被忽略造成的估计偏差？
- 方法与思路
- 作者将原用于离散结果测量误差问题的估计似然方法扩展至连续结果与结果依赖抽样，意在纳入全部可用信息。
- 材料陈述的贡献
- 摘要称，论文推导估计量的渐近性质，模拟显示中等样本下渐近结果近似有限样本表现，并与已有适用方法进行比较。
- 阅读关注点
- 应核对抽样概率的处理、暴露分布和回归模型假设、未测量暴露者如何贡献似然，以及模拟比较中的效率与偏差指标。
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- 仅据版本未注明的 OpenAlex 索引摘要；具体抽样设计、模型条件及比较数值未说明，也未明确声称所提方法在所有情形下优于已有方法。
原文摘要
Many biomedical observational studies attempt to relate a continuous outcome to an environmental exposure and other important covariates. If the outcome is easier or cheaper to measure relative to the exposure of interest, then the outcome may be observed for every member of a finite-study population, whereas exposure measurements may be obtained only for a relatively small subsample of this population. Rather than selecting a simple random subsample of individuals for exposure measurement, investigators may attempt to enhance study efficiency by allowing the selection probabilities to depend on the observed outcomes; we refer to such sampling schemes as outcome-dependent sampling (ODS). Standard estimation methods that ignore the ODS design will yield biased and inconsistent parameter estimates. Furthermore, it is generally desirable to use estimators that incorporate all available data as analyses restricted to subjects with complete information are inefficient. To this end, we extend an estimated likelihood method, originally developed for discrete outcome measurement error problems in which accurate exposure measurements are made only for a simple random “validation” sample, to allow for continuous outcomes and ODS designs. We derive the asymptotic properties of the proposed estimator and use simulated data to show that the asymptotic results closely approximate the finite-sample properties in samples of moderate size. We also use simulated data to compare the performance of our proposed estimator with that of existing methods applicable to the ODS problem.