Abstract：
The combination of propensity score analysis and multiple imputation has been prominent in epidemiological research in recent years. However, studies on the evaluation of balance in this combination are limited. In this paper, we propose a new method for assessing balance in propensity score analysis following multiple imputation. A simulation study was conducted to evaluate the performance of balance assessment methods (Leyrat\'s, Leite\'s, and new method). Simulated scenarios varied regarding the presence of missing data in the control or treatment and control group, and the imputation model with/without outcome. Leyrat\'s method was more biased in all the studied scenarios. Leite\'s method and the combine method yielded balanced results with lower mean absolute difference, regardless of whether the outcome was included in the imputation model or not. Leyrat\'s method had a higher false positive ratio and Leite\'s and combine method had higher specificity and accuracy, especially when the outcome was not included in the imputation model. According to simulation results, most of time, Leyrat\'s method and Leite\'s method contradict with each other on appraising the balance. This discrepancy can be solved using new combine method.
摘要：
近年来，倾向评分分析和多重归因相结合在流行病学研究中一直很突出。然而,关于这种组合中平衡评价的研究是有限的。在本文中,我们提出了一种在多重归集后的倾向得分分析中评估平衡的新方法。进行了一项模拟研究，以评估平衡评估方法的性能(Leyrat's，Leite\'s,和新方法)。模拟场景因对照组或治疗和对照组中缺失数据的存在而有所不同，以及有/无结果的插补模型。Leyrat的方法在所有研究的场景中都更有偏见。Leite方法和组合方法产生的平衡结果具有较低的平均绝对差异，无论结果是否包含在填补模型中.Leyrat's法具有较高的假阳性率,Leite's法和联合法具有较高的特异性和准确性,特别是当结果不包括在插补模型中时。根据仿真结果,大部分时间,Leyrat的方法和Leite的方法在平衡评估上相互矛盾。这种差异可以使用新的组合方法来解决。
