论文标题
REMIOD：基于参考的纵向二元和顺序结果的受控多重插补具有不可忽视的遗失
Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness
论文作者
论文摘要
缺少有关响应变量的数据在临床研究中很常见。与缺失机制的不确定性相对应，已经开发了有关插补的理论框架。实际上，建议在缺失数据的主要假设下进行统计有效的分析，然后在替代假设下进行敏感性分析，以评估结果的鲁棒性。由于软件的可用性，受控的多个插补（MI）程序（包括基于DELTA的方法和基于参考的方法）已成为分析缺失的不交流假设下的连续变量的流行。但是，类似的工具仍然将这些方法应用于分类数据。在本文中，我们介绍了r package \ textbf {remiod}，该{remiod}利用贝叶斯框架在二进制和序列结果的回归模型中执行插补。在概述了理论背景，使用示例描述并说明了\ textbf {remiod}的用法和特征。
Missing data on response variables are common in clinical studies. Corresponding to the uncertainty of missing mechanism, theoretical frameworks on controlled imputation have been developed. In practice, it is recommended to conduct a statistically valid analysis under the primary assumptions on missing data, followed by sensitivity analysis under alternative assumptions to assess the robustness of results. Due to the availability of software, controlled multiple imputation (MI) procedures, including delta-based and reference-based approaches, have become popular for analyzing continuous variables under missing-not-at-random assumptions. Similar tools, however, still limit application of these methods to categorical data. In this paper, we introduce the R package \textbf{remiod}, which utilizes the Bayesian framework to perform imputation in regression models on binary and ordinal outcomes. Following outlining theoretical backgrounds, usage and features of \textbf{remiod} are described and illustrated using examples.
