The randomized pretest posttest design is common in psychology, as is the corresponding missing data concern. Although missing data handling has seen advances over the past several decades, effective and practical solutions for handling missing data in randomized pretest posttest designs are lacking, particularly when assumptions of commonly used statistical models are violated. Although analysis of covariance can capture the average treatment effect with complete data, even when assumptions are tenuous, this becomes more difficult with missing data. This investigation fills this gap in the literature by comparing a variety of analysis models for estimating the average treatment effect under violations of linearity and homogeneity of regression slopes, when data are missing by several plausible, but understudied, missing at random patterns for randomized pretest posttest studies. Two missing data handling techniques, listwise deletion and multiple imputation, were considered. Listwise deletion provided maximum likelihood estimates (unbiased and appropriately precise) of the average treatment effect as long as the analysis model was appropriately specified to handle the violated assumption and the pretest mean was estimated using all cases. Although multiple imputation was effective as long as the imputation model was correct, the results highlight to the importance of model specification in the context of missing data. Importantly, the specific pattern of missing at random data had implications for results, emphasizing the need to consider the particular pattern of missingness beyond the general appropriateness of the missing at random assumption. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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随机前测后测研究中回归的非线性和异质性的模型规范：缺失数据的实用解决方案。

随机前测后测设计在心理学中很常见，相应的缺失数据问题也是如此。尽管缺失数据处理在过去几十年中取得了进步，但缺乏在随机前测后测设计中处理缺失数据的有效且实用的解决方案，尤其是在违反常用统计模型的假设时。尽管协方差分析可以用完整的数据捕捉平均治疗效果，即使假设很脆弱，但如果缺少数据，这会变得更加困难。本研究通过比较各种分析模型来填补文献中的这一空白，这些模型用于估计在违反回归斜率的线性和同质性的情况下的平均治疗效果，当数据因几个似是而非的但未充分研究而丢失时，缺少随机前测后测研究的随机模式。考虑了两种缺失数据处理技术，列表删除和多重插补。只要适当地指定分析模型以处理违反的假设并且使用所有情况估计预测试均值，则逐列删除提供平均治疗效果的最大似然估计（无偏且适当精确）。尽管只要插补模型正确，多重插补就是有效的，但结果强调了模型规范在缺失数据的背景下的重要性。重要的是，随机数据缺失的特定模式对结果有影响，强调需要考虑特定的缺失模式，超出随机缺失假设的一般适当性。