Dealing with missing data in longitudinal studies

纵向研究--对个人进行长达数月或数年的跟踪研究--对于了解人们的生活方式或环境的各个方面如何影响他们的疾病风险非常重要。当对个体进行较长时间的跟踪时，对特定变量的测量有时不可避免地会丢失，例如，因为测量仪器故障，或者受试者没有回答某些问题或没有参加考试。缺失值使纵向研究的数据分析变得更加复杂，因为它们可能导致结果既有偏见(它们与如果考虑缺失值将观察到的结果不同)又低效(它们的精确度低于如果考虑缺失值的结果)。已经提出了解决这些问题的新的统计方法，并有可能在纵向研究的分析中减少偏差和提高效率。然而，这些方法可能很复杂，目前几乎没有使用它们的实际经验。我们正在调查应用这些方法的实际问题，特别是一种称为多重归罪的方法，以证明它们在什么情况下是有用的，制定选择模型并确认它们是否合适的战略，并比较不同的方法和软件。除了在科学期刊上发表这项研究的结果外，我们还将为未来使用这些方法的人制定指南。

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 risk of disease. When 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. Missing values make analyses of data from longitudinal studies more complicated, because they can lead to results that are both biased (they differ from the results that would be observed if the missing values were taken into account) and inefficient (they are less precise than they would be if missing values were taken into account). New statistical methods that 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 complex and there is currently little practical experience of their use. We are investigating practical issues in applying these methods, in particular a method called multiple imputation, in order to demonstrate the circumstances in which they are useful, develop strategies for choosing models and confirming that they are appropriate, and compare different approaches and software. As well as publishing the results of the study in scientific journals, we will develop guidelines for people who use the methods in the future.