Split Samples and Design Sensitivity in Observational Studies
Split Samples and Design Sensitivity in Observational Studies
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DOI：
10.1198/jasa.2009.tm08338
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发表时间：
2009-09-01
影响因子：
3.7
通讯作者：
Small, Dylan S.
中科院分区：
文献类型：
--
作者：
Heller, Ruth;Rosenbaum, Paul R.;Small, Dylan S.
治疗效应的观察性或非随机化研究可能因未能控制某些未测量的相关协变量而产生偏倚。众所周知，观察性研究的设计会强烈影响其对未观察到的协变量偏倚的敏感性。譬如说选择一个结果进行研究，或者决定在一致性测试中将联合收割机几个结果结合起来，可以实质上影响对未观察到的偏差的敏感性。因此，塑造设计的决策至关重要，但在缺乏数据的情况下，这些决策也很难做出。我们考虑将观察性研究的数据随机分为较小的计划样本和较大的分析样本的可能性，其中计划样本用于指导设计决策。在回顾了设计灵敏度的概念之后。我们在理论上，通过数值计算，并通过模拟来评估样本分裂，将其与使用所有数据的几种方法进行比较。样本分割是非常有效的，在观察性研究中比在随机实验中更有效：将1,000个匹配对分成100个计划对和900个分析对通常会大大提高设计灵敏度。遗传毒理学的一个例子被用来说明该方法。
An observational or nonrandomized study of treatment effects may be biased by failure to control for some relevant covariate that was not measured. The design of an observational study is known to strongly affect its sensitivity to biases from covariates that were not observed. For instance. the choice of an outcome to study, or the decision to combine several outcomes in a test for coherence, can materially affect the sensitivity to unobserved biases. Decisions, that shape the design are, therefore, critically important, but they are also difficult decisions to make in the absence of data. We consider the possibility of randomly splitting the data from an observational study into a smaller planning sample and a larger analysis sample, where the planning sample is used to guide decisions about design. After reviewing the concept of design sensitivity. we evaluate sample splitting in theory, by numerical computation, and by simulation, comparing it to several methods that use all of the data. Sample splitting is remarkably effective, much more so in observational studies than in randomized experiments: splitting 1,000 matched pairs into 100 planning pairs and 900 analysis pairs often materially improves the design sensitivity. An example from genetic toxicology is used to illustrate the method.