Institute for Clinical Trials and Methodology, University College London, London, UK.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Stat Med. 2022 Nov 10;41(25):5000-5015. doi: 10.1002/sim.9549. Epub 2022 Aug 12.
Substantive model compatible multiple imputation (SMC-MI) is a relatively novel imputation method that is particularly useful when the analyst's model includes interactions, non-linearities, and/or partially observed random slope variables.
Here we thoroughly investigate a SMC-MI strategy based on joint modeling of the covariates of the analysis model. We provide code to apply the proposed strategy and we perform an extensive simulation work to test it in various circumstances. We explore the impact on the results of various factors, including whether the missing data are at the individual or cluster level, whether there are non-linearities and whether the imputation model is correctly specified. Finally, we apply the imputation methods to the motivating example data.
SMC-JM appears to be superior to standard JM imputation, particularly in presence of large variation in random slopes, non-linearities, and interactions. Results seem to be robust to slight mis-specification of the imputation model for the covariates. When imputing level 2 data, enough clusters have to be observed in order to obtain unbiased estimates of the level 2 parameters.
SMC-JM is preferable to standard JM imputation in presence of complexities in the analysis model of interest, such as non-linearities or random slopes.
实质性模型兼容多重插补（SMC-MI）是一种相对较新的插补方法，当分析师的模型包括交互作用、非线性和/或部分观测随机斜率变量时，这种方法特别有用。
在这里，我们彻底研究了一种基于分析模型协变量联合建模的 SMC-MI 策略。我们提供了应用所提出策略的代码，并进行了广泛的模拟工作，以在各种情况下对其进行测试。我们探讨了各种因素对结果的影响，包括缺失数据是在个体还是群组层面上、是否存在非线性以及插补模型是否正确指定。最后，我们将插补方法应用于激励示例数据。
SMC-JM 似乎优于标准 JM 插补，尤其是在随机斜率、非线性和交互作用存在较大差异的情况下。结果似乎对协变量插补模型的轻微误设定具有稳健性。当插补 2 级数据时，必须观察到足够多的群组，以便获得 2 级参数的无偏估计。
在分析模型存在复杂性（如非线性或随机斜率）的情况下，SMC-JM 优于标准 JM 插补。
