Department of Biostatistics, University of Washington, Seattle, WA, USA.
Department of Epidemiology, University of Washington, Seattle, WA, USA.
Clin Trials. 2022 Aug;19(4):380-383. doi: 10.1177/17407745221084702. Epub 2022 Mar 8.
Stepped wedge cluster randomized trials are often analysed using linear mixed effects models that may include random effects for cluster, time and/or treatment. We investigate the impact of misspecification of the random effects structure of the model. Specifically, we considered two cases of misspecification of the random effects in a cross-sectional stepped wedge cluster randomized trials model - fit a linear mixed effects model with random time effects but the true model includes random treatment effects (case 1) or fit a linear mixed effects model with random treatment effect but the true model includes random time effects (case 2) - and derived the variance of the estimated treatment effect under misspecification. We defined two measures of the effect of misspecification: validity and efficiency. Validity is the ratio of the model-based variance of the treatment effect from the mis-specified model divided by the true variance of the treatment effect from the mis-specified model (based on a sandwich estimate of the variance). Efficiency is the ratio of the model-based variance of the treatment effect from the correctly specified model divided by the true variance of the treatment effect from the mis-specified model. We found that validity is less than 1.0 (anti-conservative) in almost all situations investigated with the exception of case 1 with two sequences, when validity could be greater than 1.0. Efficiency is less than 1 in all cases and depends on the intracluster correlation coefficient, the relative magnitude of the variance of the misclassified variance component, and the number of sequences. In general, there is no universal recommendation as to the most robust approach except for the case of a classic stepped wedge cluster randomized trial with only 2 sequences, where fitting a random time model is less likely to lead to anti-conservative inference compared with fitting a random intervention model.
阶梯式楔形群随机临床试验通常使用线性混合效应模型进行分析，该模型可能包括聚类、时间和/或治疗的随机效应。我们研究了模型的随机效应结构指定不当的影响。具体来说，我们考虑了两种情况下的模型的随机效应的指定不当-拟合具有随机时间效应的线性混合效应模型，但真实模型包括随机治疗效应（情况 1）或拟合具有随机治疗效应的线性混合效应模型，但真实模型包括随机时间效应（情况 2）-并推导出在指定不当的情况下估计治疗效果的方差。我们定义了两种指定不当效果的度量：有效性和效率。有效性是从指定不当的模型中基于模型的治疗效果的方差与从指定不当的模型中治疗效果的真实方差的比值（基于方差的夹心估计）。效率是从正确指定的模型中基于模型的治疗效果的方差与从指定不当的模型中治疗效果的真实方差的比值。我们发现，在几乎所有研究的情况下，有效性都小于 1.0（反保守），除了两种情况下的情况 1，此时有效性可能大于 1.0。在所有情况下，效率都小于 1，并且取决于簇内相关系数、错误分类方差分量的方差的相对大小以及序列的数量。一般来说，除了只有 2 个序列的经典阶梯式楔形群随机临床试验的情况外，没有普遍推荐最稳健的方法，在这种情况下，拟合随机时间模型不太可能导致反保守的推断，而拟合随机干预模型则更有可能。
