Aikens Rachael C, Greaves Dylan, Baiocchi Michael
Program in Biomedical Informatics, Stanford University, Stanford, California, USA.
Department of Statistics, Stanford University, Stanford, California, USA.
Stat Med. 2020 Dec 30;39(30):4821-4840. doi: 10.1002/sim.8754. Epub 2020 Oct 5.
Observational studies often benefit from an abundance of observational units. This can lead to studies that-while challenged by issues of internal validity-have inferences derived from sample sizes substantially larger than randomized controlled trials. But is the information provided by an observational unit best used in the analysis phase? We propose the use of a "pilot design," in which observations are expended in the design phase of the study, and the posttreatment information from these observations is used to improve study design. In modern observational studies, which are data rich but control poor, pilot designs can be used to gain information about the structure of posttreatment variation. This information can then be used to improve instrumental variable designs, propensity score matching, doubly robust estimation, and other observational study designs. We illustrate one version of a pilot design, which aims to reduce within-set heterogeneity and improve performance in sensitivity analyses. This version of a pilot design expends observational units during the design phase to fit a prognostic model, avoiding concerns of overfitting. In addition, it enables the construction of "assignment-control plots," which visualize the relationship between propensity and prognostic scores. We first show some examples of these plots, then we demonstrate in a simulation setting how this alternative use of the observations can lead to gains in terms of both treatment effect estimation and sensitivity analyses of unobserved confounding.
观察性研究通常受益于大量的观察单位。这可能导致一些研究，尽管受到内部有效性问题的挑战，但从样本量得出的推论比随机对照试验大得多。但是，观察单位提供的信息在分析阶段得到最佳利用了吗？我们建议使用“试点设计”，即在研究的设计阶段投入观察，然后利用这些观察的治疗后信息来改进研究设计。在现代观察性研究中，数据丰富但对照不足，试点设计可用于获取有关治疗后变异结构的信息。然后，这些信息可用于改进工具变量设计、倾向得分匹配、双重稳健估计和其他观察性研究设计。我们展示了一种试点设计版本，其目的是减少组内异质性并提高敏感性分析的性能。这种试点设计版本在设计阶段投入观察单位以拟合预后模型，避免了过度拟合的担忧。此外，它能够构建“分配 - 对照图”，直观显示倾向得分和预后得分之间的关系。我们首先展示这些图的一些示例，然后在模拟环境中证明这种对观察的另类使用如何在治疗效果估计和未观察到的混杂因素的敏感性分析方面带来收益。
