Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable missingness. Unbiased estimation with standard implementations of multiple imputation depends on assumptions concerning the missingness mechanism (e.g. that data are "missing at random"). The plausibility of these assumptions can only be assessed using subject-matter knowledge, and not data alone. It is therefore important to perform sensitivity analyses to explore the robustness of results to violations of these assumptions (e.g. if the data are in fact "missing not at random"). In this tutorial, we provide a roadmap for conducting sensitivity analysis using the Not at Random Fully Conditional Specification (NARFCS) procedure for multivariate imputation. Using a case study from the Longitudinal Study of Australian Children, we work through the steps involved, from assessing the need to perform the sensitivity analysis, and specifying the NARFCS models and sensitivity parameters, through to implementing NARFCS using FCS procedures in R and Stata.

翻译：多重插补是处理缺失数据的常用方法，其中完全条件设定（FCS）是多变量缺失情况下的主流插补方法之一。标准多重插补实现的无偏估计依赖于关于缺失机制的假设（例如数据“随机缺失”）。这些假设的合理性只能通过领域专业知识进行评估，而无法仅凭数据本身判断。因此，执行敏感性分析以探究结果在这些假设被违反时（例如数据实际上“非随机缺失”）的稳健性至关重要。本教程通过澳大利亚儿童纵向研究的案例，提供了使用非随机完全条件设定（NARFCS）程序进行多变量插补敏感性分析的路线图。我们从评估敏感性分析的必要性、指定NARFCS模型与敏感性参数开始，逐步演示如何通过R和Stata中的FCS程序实现NARFCS。