An estimated likelihood method for continuous outcome regression models with outcome-dependent sampling
An estimated likelihood method for continuous outcome regression models with outcome-dependent sampling
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DOI：
10.1198/016214504000001853
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发表时间：
2005-06-01
影响因子：
3.7
通讯作者：
Zhou, HB
中科院分区：
文献类型：
--
作者：
Weaver, MA;Zhou, HB
许多生物医学观察研究试图将连续结果与环境暴露和其他重要协变量联系起来。如果相对于感兴趣的暴露，测量结果更容易或更便宜，那么可以观察有限研究人群中的每个成员的结果，而暴露测量只能针对该人群中相对较小的亚样本进行。研究人员可能会试图通过允许选择概率取决于观察到的结果来提高研究效率，而不是选择简单的随机个体子样本进行暴露测量；我们将这种抽样方案称为结果相关抽样(Ods)。忽视消耗臭氧层物质设计的标准估计方法将产生有偏见和不一致的参数估计。此外，通常希望使用包含所有可用数据的估计器，因为仅限于具有完整信息的受试者的分析效率很低。为此，我们扩展了一种估计似然方法，该方法最初是为离散结果测量误差问题而开发的，在这种方法中，只对简单的随机“验证”样本进行准确的暴露测量，以允许连续的结果和ods设计。我们推导了所提出的估计量的渐近性质，并用模拟数据表明，在中等大小的样本下，渐近结果接近有限样本的性质。我们还使用模拟数据来比较我们所提出的估计器的性能与现有适用于ODS问题的方法的性能。
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.