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

纵向研究？研究中对个体进行了数月或数年的跟踪？对了解人类的各个方面都很重要人们的生活方式或环境会影响他们的健康和福祉。当对许多个体进行长期随访时，不可避免的是，有时会丢失特定变量的测量结果，例如，由于测量设备故障，或者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，未能适当解决这些问题可能会导致结果偏倚（与包含缺失值时观察到的结果不同）和效率低下（与包含缺失值时相比，结果存在更多的不确定性）。已经提出了解决这些问题的新的统计方法，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，难以应用，并且在某些情况下，它们的不正确使用实际上可能会增加偏倚。我们将制定解决方案，以解决应用这些方法之一（多重插补）的剩余问题，包括制定策略，以确定缺失值是否可能导致分析偏倚，并检查多重插补模型是否合适。我们还将开发新方法，即使在所选统计模型的某些方面不正确时，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，以最大限度地提高其未来的使用价值，并将结果发表在科学期刊上。

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.