Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia
Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia.
BMJ Open. 2023 Feb 1;13(2):e065576. doi: 10.1136/bmjopen-2022-065576.
Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The objective of this scoping review is to examine the use of MI in observational studies that address causal questions, with a focus on if and how (a) missingness assumptions are expressed and assessed, (b) missingness assumptions are used to justify the choice of a complete case analysis and/or MI for handling missing data and (c) sensitivity analyses under alternative plausible assumptions about the missingness mechanism are conducted.
We will review observational studies that aim to answer causal questions and use MI, published between January 2019 and December 2021 in five top general epidemiology journals. Studies will be identified using a full text search for the term 'multiple imputation' and then assessed for eligibility. Information extracted will include details about the study characteristics, missing data, missingness assumptions and MI implementation. Data will be summarised using descriptive statistics.
Ethics approval is not required for this review because data will be collected only from published studies. The results will be disseminated through a peer reviewed publication and conference presentations.
This protocol is registered on figshare (https://doi.org/10.6084/m9.figshare.20010497.v1).
健康相关研究中的观察性研究通常旨在回答因果问题。这些研究中经常存在缺失数据，并且通常在多个变量中出现，例如暴露、结局和/或用于控制混杂因素的变量。缺失数据的标准分类为完全随机缺失、随机缺失（MAR）或非随机缺失，当缺失出现在多个变量中时，这种分类无法清晰评估缺失假设。这给选择分析方法和确定在合理替代缺失数据假设下是否需要进行敏感性分析带来了挑战。这在多重插补（MI）中尤为相关，通常通过假设数据为 MAR 来证明 MI 的合理性。本综述的目的是检查在解决因果问题的观察性研究中使用 MI 的情况，重点关注是否以及如何：（a）表达和评估缺失假设，（b）使用缺失假设来证明选择完整案例分析和/或 MI 来处理缺失数据的合理性，以及（c）进行缺失机制的替代合理假设下的敏感性分析。
我们将回顾 2019 年 1 月至 2021 年 12 月期间在五本顶级一般流行病学杂志上发表的旨在回答因果问题并使用 MI 的观察性研究。将通过全文搜索“多重插补”一词来识别研究，并评估其合格性。提取的信息将包括研究特征、缺失数据、缺失假设和 MI 实施的详细信息。数据将使用描述性统计进行总结。
本综述不需要伦理批准，因为数据仅从已发表的研究中收集。研究结果将通过同行评议的出版物和会议报告进行传播。
本方案在 figshare 上注册（https://doi.org/10.6084/m9.figshare.20010497.v1）。
