论文标题
分析并非随机丢失的重复测量数据的替代方法
Alternative approaches for analysing repeated measures data that are missing not at random
论文作者
论文摘要
我们考虑研究结果随着时间的时间收集的多项措施，但是一些受试者在随访结束前退出。对此类数据的分析通常是根据“最后一次观察”或“随机丢失”假设进行的。我们考虑了两种识别的替代策略。第一个与因果推论文献中的差异差异方法密切相关。第二种可以纠正违反平行趋势假设的行为，只要可以访问有效的“定制仪器变量”即可。将这些与现有方法进行比较，首先是概念上，然后在弗雷明汉心脏研究的数据分析中进行了比较。
We consider studies where multiple measures on an outcome variable are collected over time, but some subjects drop out before the end of follow up. Analyses of such data often proceed under either a 'last observation carried forward' or 'missing at random' assumption. We consider two alternative strategies for identification; the first is closely related to the difference-in-differences methodology in the causal inference literature. The second enables correction for violations of the parallel trend assumption, so long as one has access to a valid 'bespoke instrumental variable'. These are compared with existing approaches, first conceptually and then in an analysis of data from the Framingham Heart Study.
