Development of miDOC: an expert system and methodology for multiple imputation

许多健康和社会研究都是通过对人的研究来完成的-例如随机试验（比较那些接受治疗的人与那些没有接受治疗的人），队列研究（检查一组人的健康如何随时间变化，以及导致这些变化的原因）或病例对照研究（检查患相对罕见疾病的风险因素）。所有这些研究都可能存在数据缺失的问题--要么是人们完全退出，要么是没有回答一些问题，要么是忘记提供一些信息。这种缺失的数据可能意味着研究结果是错误的（“有偏见”），或者它们比它们应该的精确度低，或者两者兼而有之。对于如何处理缺失数据已经做了大量的研究，其中一种常用的方法是多重插补（MI）。在MI中，其他信息（例如，某人先前健康状况的详细信息，以及他们目前正在使用的药物）用于预测（“插补”）缺失的信息。这项技术的成功关键取决于为什么信息首先会丢失，以及丢失的信息可以预测到什么程度。有一些指导研究人员进行MI，但有些指导方针是不正确的，有些是复杂的，很难遵循。不同的研究人员以不同的方式使用多元智能，并且通常不记录他们所做的-所以很难复制分析，或者看看分析师是否遵循了最佳实践。我们将评估当人们使用错误的分析模型时会引起什么问题;在插补模型中包含一些不能很好地预测缺失信息的变量可能会引起什么问题;如何选择在插补模型中包含哪些其他变量。我们还将调查研究人员如何最好地检查他们的MI是否工作良好。然后，我们将把这些新方法与现有的知识，到一个新的自动化专家系统，“多重插补医生”（miDOC）。miDOC将指导研究人员进行分析，检查数据集的结构，以建议是否需要多重插补，如果需要，如何执行。专家系统miDOC将对所有使用不完整数据的研究人员有用，但将特别针对那些在缺失数据统计分析方面接受过相对较少正式培训的研究人员。miDOC不仅能让用户获得专家的分析建议，而且通过提供记录的决策和代码，它将提高分析的可重复性和透明度。我们将与研究人员一起组织焦点小组，帮助我们开发miDOC，并根据反馈进行完善。我们将免费提供miDOC，并将有关miDOC的方法和信息包含在我们已经运行的关于如何处理缺失数据的课程中，在www.missing data.org.uk和由共同申请人之一撰写的教科书的第二版中。我们将举办两个免费研讨会（将永久在线提供），以帮助尽可能多的人从这些方法和miDOC中受益。这些方法和miDOC将适用于所有类型的研究-随机试验，队列研究，病例对照研究-因此有可能改善健康和医学等领域的许多研究。我们将利用我们与其他队列，学术和非学术机构的联系，以确保我们的方法被广泛使用，从而提高英国和全球政策和实践的证据水平。

Much health and social research is done using studies of people - e.g. randomised trials (comparing those who do have a treatment to those who don't), cohort studies (examining how the health of a group of people changes over time, and what causes these changes) or case-control studies (examining the risk factors for getting a relatively rare disease). All these studies can suffer from missing data - either when people drop out completely, or don't answer some questions, or forget to give some information. This missing data can mean that the results of the study are wrong ("biased"), or that they are less precise than they should be, or both. Much research has been done into how to deal with missing data, and one commonly-used method is multiple imputation (MI). In MI, other information (e.g. details of someone's previous health, and medications they are currently using) is used to predict ("impute") the missing information. The success of this technique depends crucially on why the information is missing in the first place, and how well the missing information can be predicted. There are guidelines for researchers carrying out MI, but some of the guidelines are not correct, and some are complex and hard to follow. Different researchers use MI in different ways, and do not usually document what they did - so it is hard to replicate analyses, or to see if analysts have followed best practice.We aim to develop methods to address some remaining issues about how to carry out MI. We will assess what problems are caused when people use the wrong analysis model; what problems may arise from including some variables in the imputation model that do not predict the missing information very well; how to choose which other variables to include in the imputation model. We will also investigate how researchers can best check whether their MI is working well. We will then pull these new methods together with existing knowledge into a new automated expert system, the 'multiple imputation Doctor' (miDOC). miDOC will guide researchers through their analyses, examining the structure of the dataset to advise on whether multiple imputation is needed, and if so how to perform it. The expert system, miDOC, will be useful for all researchers using incomplete data, but will be particularly aimed at those who may have relatively little formal training in statistical analysis of missing data. Not only will miDOC give users access to expert advice on their analysis, but by providing documented decisions and code it will increase reproducibility and transparency of analyses.We will run focus groups with researchers to help us develop miDOC, and refine it on the basis of feedback. We will make miDOC freely available, and also include the methods and information about miDOC in courses we already run on how to deal with missing data, on www.missing data.org.uk and in the second edition of a textbook authored by one of the co-applicants. We will run two free workshops (which will be permanently made available online), in order to help as many people as possible benefit from these methods and miDOC. The methods and miDOC will be useful for all types of study - randomised trials, cohort studies, case-control studies - and thus have the potential to improve much research in both health and medicine, and beyond. We will use our links with other cohorts, academic and non-academic agencies to ensure that our methods are widely used, and thus improve the level of evidence informing policy and practice in the UK and worldwide.