Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis

There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials, and use multiple imputation to fill in missing measurements. We apply our method to 5 longitudinal adolescent depression trials where 4 studies used one depression measure and the fifth study used a different depression measure. None of the 5 studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigating whether external information is appropriately incorporated into the imputed values.

个体参与者数据荟萃分析在整合多研究数据方面具有诸多优势，包括增强检测效应的能力、提高样本异质性的增加，以及相较于仅依赖于已发表结果进行的荟萃分析，能够执行更为复杂的分析。然而，一个根本的挑战在于，在待合并的各研究中，感兴趣的变量很可能以不同的方式进行测量。我们提出，这种情况可以被视为一个缺失数据问题，其中某些试验中的某些结果完全缺失，并采用多重插补法来填补缺失的测量值。我们将我们的方法应用于5项纵向青少年抑郁症试验，其中4项研究使用了同一抑郁症测量方法，而第5项研究使用了不同的抑郁症测量方法。这5项研究中，没有一项包含这两种抑郁症测量方法。我们描述了一种多重插补方法，用于填补缺失的抑郁症测量值，该方法利用了外部校准研究，其中使用了这两种抑郁症测量方法。我们讨论了在开发插补模型时的一些实际问题，包括考虑治疗组和研究因素。我们提出了用于检查插补模型拟合度和调查外部信息是否适当融入估计值的诊断方法。