Multiple imputation by chained equations for data that are missing not at random: methods development for randomised trials and observational studies

通过链式方程对非随机丢失的数据进行多重插补：随机试验和观察性研究的方法开发

• 批准号: MC_EX_MR/M025012/1
• 负责人: Ian White
• 金额: $ 21.25万
• 依托单位: MRC Centre Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MC_EX_MR%2FM025012%2F1
• 关键词: Multiple; imputation; chained; equations; data

Medical researchers often find that some data which they intended to collect could not be collected: for example, because participants could not be contacted or were unwilling to provide data. These missing data present problems in the analysis of the study, because including only participants who provided data may lead to incorrect results. The commonest way to handle missing data assumes that missing values are similar to observed values within subgroups: for example, for participants whose weight was observed at times 1 and 2 but missing at time 3, the missing weights at time 3 are assumed to have the same average as observed weights at time 3 in participants whose weights were similar at times 1 and 2 and observed at time 3. This approach is called "Missing at Random" and provides a good starting point for analysis but is unlikely to be entirely correct: for example, participants whose weight was unobserved at time 3 may have had a larger weight gain. It is therefore important for researchers to do sensitivity analyses in which different assumptions are made about the missing data. Our research proposes to adapt a popular method for handling missing data called Multiple Imputation by Chained Equations (MICE) to allow for a range of assumptions about the missing data. The idea of this approach is that missing values are filled in iteratively using the relationships between all the variables, and this is then done multiple times in order to express uncertainty about the missing data. However, at present the MICE method is done assuming Missing at Random. We have developed a new way to implement the MICE method which does not assume Missing at Random: instead, the researcher has to specify how big the departures from Missing at Random are, by specifying the likely average differences between missing values and observed values within subgroups. However, we have only explored the new method in idealised settings, and in particular we have not explored its use in randomised trials or in studies where outcomes are measured over time.The work will first extend the statistical theory to handle outcomes that are measured over time and see how well the method performs in randomised trials. It will then extend the methods to tackle a wide range of problems met in practice: for example different types of variables, complex analysis questions, and very large data sets. This work will be supported by writing user-friendly software to implement the new method in two widely used statistics packages. We will implement the method in practice in several data sets, including the Avon Longitudinal Study of Parents and Children where we will explore predictors of self-harm, and randomised trials in smoking cessation and weight loss. Missing self-harm, smoking cessation and weight loss data are all very unlikely to be Missing at Random: we will use our subject matter expertise to specify a range of likely average differences between missing values and observed values within subgroups and hence reach more defensible conclusions. This work is likely to raise unexpected theoretical issues which we will address.Finally, we believe that this method will be widely applicable, so we will disseminate it to researchers via tutorial articles and by running courses.

医学研究人员经常发现他们打算收集的一些数据无法收集：例如，因为无法联系参与者或不愿意提供数据。这些缺少的数据在研究分析中呈现出问题，因为仅包括提供数据的参与者可能会导致结果不正确。处理丢失数据的最常见方法假设缺失值类似于亚组中的观察值：例如，对于在时间1和2时观察到的体重但在时间3时丢失的参与者，假定时间3的缺少权重与观察到的时间3相同的平均值与参与者的时间3相同，他们在时间1和2的参与者的体重在时间1和2中均不在分析。例如，时间3在时间3中未观察到的参与者的体重增加可能会增加。因此，对于研究人员而言，重要的是进行敏感性分析，其中对丢失数据做出了不同的假设。我们的研究提议适应一种流行的方法来处理丢失的数据，称为链式方程式（小鼠），以允许有关丢失数据的一系列假设。这种方法的想法是，使用所有变量之间的关系在迭代中填充缺失值，然后多次进行此操作，以表达有关缺失数据的不确定性。但是，目前已经完成了小鼠方法，假设随机丢失。我们已经开发了一种新的方法来实现小鼠方法，该方法不假定随机缺失：相反，研究人员必须通过指定子组中缺失值和观察值之间的可能平均差异来指定随机丢失的随机差异。但是，我们仅在理想化的设置中探索了新方法，特别是我们尚未探讨其在随机测量结果的研究中的用途。该工作将首先扩展统计理论以处理随着时间的推移测量的结果，并在随机试验中观察该方法的表现如何。然后，它将扩展解决实践中遇到的广泛问题的方法：例如，不同类型的变量，复杂的分析问题和非常大的数据集。这项工作将通过编写用户友好的软件来支持，以在两个广泛使用的统计软件包中实现新方法。我们将在几个数据集中实施该方法，包括对父母和孩子的雅芳纵向研究，我们将探索自我伤害的预测指标，以及在戒烟和体重减轻方面的随机试验。缺少自我伤害，戒烟和减肥数据都不太可能随机丢失：我们将使用我们的主题专业知识来指定子组中缺失值和观察值之间可能的平均差异，因此得出更可辩护的结论。这项工作可能会引发我们将要解决的意外理论问题。从本文中，我们认为该方法将广泛适用，因此我们将通过教程文章和跑步课程将其传播给研究人员。

项目成果

Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies.

• DOI: 10.1093/aje/kwy173
• 发表时间: 2018-12-01
• 期刊: American journal of epidemiology
• 影响因子: 5
• 作者: Moreno-Betancur M;Lee KJ;Leacy FP;White IR;Simpson JA;Carlin JB
• 通讯作者: Carlin JB

A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data.

• DOI: 10.1002/sim.8584
• 发表时间: 2020-09-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Tompsett D;Sutton S;Seaman SR;White IR
• 通讯作者: White IR

New models for describing outliers in meta-analysis.

• DOI: 10.1002/jrsm.1191
• 发表时间: 2016-09
• 期刊: Research synthesis methods
• 影响因子: 9.8
• 作者: Baker R;Jackson D
• 通讯作者: Jackson D

The design-by-treatment interaction model: a unifying framework for modelling loop inconsistency in network meta-analysis.

• DOI: 10.1002/jrsm.1188
• 发表时间: 2016-09
• 期刊: Research synthesis methods
• 影响因子: 9.8
• 作者: Jackson D;Boddington P;White IR
• 通讯作者: White IR

Borrowing of strength and study weights in multivariate and network meta-analysis.

• DOI: 10.1177/0962280215611702
• 发表时间: 2017-12
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Jackson D;White IR;Price M;Copas J;Riley RD
• 通讯作者: Riley RD