Developing and disseminating robust methods for handling missing data in epidemiological studies

纵向研究？对个体进行数月或数年随访的研究？对理解人的方方面面都很重要？美国人的生活方式或环境会影响他们的健康和幸福。当对许多人进行长时间的跟踪调查时，不可避免地会出现对特定变量的测量有时会丢失的情况，例如，由于测量装置坏了，或者由于受试者没有回答某些问题或没有参加考试。个人也可能完全退出研究。缺失值在纵向研究的数据分析中提出了困难的问题，如果不能适当地解决这些问题，可能会导致结果有偏差（如果缺失值可以被包括在内，它们与观察到的结果不同）和效率低下（结果的不确定性比如果缺失值可以被包括在内）。新的统计方法确实解决了这些问题，并有可能减少偏倚，提高纵向研究分析的效率。然而，这些方法可能非常复杂且难以应用，并且在某些情况下，它们的错误使用实际上可能会增加偏差。我们将为应用其中一种方法（多重输入）的剩余问题开发解决方案，包括制定策略来确定缺失值是否可能导致分析中的偏差，并检查多重输入模型是否合适。我们还将开发新的方法，即使所选择的统计模型的某些方面是不正确的，这些方法仍然有效。我们将把我们的新方法整合到现有的软件中，以最大限度地利用它们的未来，并在科学期刊上发表结果。

Longitudinal studies ? studies in which individuals are followed over periods of many months or years ? are of great importance in understanding how aspects of people?s lifestyle or environment influence their health and wellbeing. When many individuals are followed over extended periods it is inevitable that measurements on particular variables are sometimes missing, for example because a measuring device broke down, or a subject did not answer certain questions or did not attend an examination. Individuals may also drop out of the study altogether. Missing values raise difficult issues in the analysis of data from longitudinal studies, and failing to address these appropriately can lead to results that are both biased (they differ from the results that would be observed if the missing values could have been included) and inefficient (there is more uncertainty about the results than there would be if the missing values could have been included). New statistical methods that do address these issues have been proposed, and have the potential to decrease bias and increase efficiency in analyses of longitudinal studies. However, these methods can be highly complex and difficult to apply, and their incorrect use may actually increase bias in certain circumstances. We will develop solutions to the remaining problems with applying one of these methods (multiple imputation), including developing strategies for deciding whether missing values are likely to cause bias in analyses, and checks for whether the multiple imputation models are appropriate. We will also develop new methods which still work even when aspects of the chosen statistical models are incorrect. We will incorporate our new methods into existing software, to maximise their future use, as well as publishing the results in scientific journals.