Scientists frequently generalize population level causal quantities such as average treatment effect from a source population to a target population. When the causal effects are heterogeneous, differences in subject characteristics between the source and target populations may make such a generalization difficult and unreliable. Reweighting or regression can be used to adjust for such differences when generalizing. However, these methods typically suffer from large variance if there is limited covariate distribution overlap between the two populations. We propose a generalizability score to address this issue. The score can be used as a yardstick to select target subpopulations for generalization. A simplified version of the score avoids using any outcome information and thus can prevent deliberate biases associated with inadvertent access to such information. Both simulation studies and real data analysis demonstrate convincing results for such selection.
翻译:科学家经常将人口水平因果数量普遍化,例如从源人口到目标人口的平均治疗效果。当因果影响各异时,源和目标人口在主题特征上的差异可能使这种概括化变得困难和不可靠。在概括化时,可以使用加权或回归来调整这种差异。但是,如果两个人口群体之间的共变分布重叠有限,这些方法通常会有很大差异。我们建议用一个通用分来解决这个问题。这个分可以作为标准,选择供概括化的目标亚人口。一个简化的分可以避免使用任何结果信息,从而可以防止与无意获取这类信息有关的蓄意偏见。模拟研究和真实数据分析都为这种选择提供了令人信服的结果。