论文标题
用于预测长期计划影响的数据融合
Data fusion for predicting long-term program impacts
论文作者
论文摘要
决策者通常需要有关计划做出决策时无法获得的计划的长期影响的信息。我们证明了如何使用数据融合方法解决了缺少最终结果的问题，并在可用数据可用之前预测了干预措施的长期影响。我们通过将干预措施与辅助长期数据相连，然后使用短期替代结果将丢失的长期结局置于，同时使用复制方法近似不确定性，从而实现了这种方法。我们使用仿真来检查方法的性能，并在案例研究中应用该方法。具体而言，我们将有关俄勒冈州健康保险实验的数据与国家纵向死亡率研究的数据融合在一起，并估计有资格申请补贴的健康保险将导致长期死亡率的统计显着改善。
Policymakers often require information on programs' long-term impacts that is not available when decisions are made. We demonstrate how data fusion methods may be used address the problem of missing final outcomes and predict long-run impacts of interventions before the requisite data are available. We implement this method by concatenating data on an intervention with auxiliary long-term data and then imputing missing long-term outcomes using short-term surrogate outcomes while approximating uncertainty with replication methods. We use simulations to examine the performance of the methodology and apply the method in a case study. Specifically, we fuse data on the Oregon Health Insurance Experiment with data from the National Longitudinal Mortality Study and estimate that being eligible to apply for subsidized health insurance will lead to a statistically significant improvement in long-term mortality.
