Statistical Methods for the Design and Analysis of Studies with Incomplete Data

缺失数据是一个常见的问题，可以在任何研究设置中遇到的，由于在研究的实施中意想不到的问题。从这样的研究中得出的结论只有在统计分析中适当处理缺失的数据时才有效。然而，在有多种机制导致失踪的情况下，统计方法不发达。如果兴趣在于由几个可能缺失的变量构造的复合分数，那么缺失的数据机制将是复杂的，模型错误规范将成为一个真正的问题。我将开发统计方法，并为这些环境中的有效分析策略提供实用建议。

Missing data is a common problem that can be encountered in any research setting due to unanticipated problems in the implementation of a study. Conclusions drawn from such a study are only valid if the missing data are appropriately handled in the statistical analyses. However, statistical methodology is underdeveloped in settings where there are multiple mechanisms contributing to missingness. If interest lies in a composite score that is constructed from several potentially missing variables, then the missing data mechanism will be complex and model misspecification becomes a real concern. I will develop statistical methodologies and provide practical recommendations for effective analysis strategies in these settings.