Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification.
Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification.
复制标题
评估效果测量修改时参数和非参数方法的性能评估。
DOI：
10.1093/aje/kwab220
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发表时间：
2022
影响因子：
5
通讯作者：
Naimi,AshleyI
中科院分区：
文献类型：
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作者：
ConzueloRodriguez,Gabriel;Bodnar,LisaM;Brooks,MariaM;Wahed,Abdus;Kennedy,EdwardH;Schisterman,Enrique;Naimi,AshleyI
效应度量修改通常使用参数模型进行评估。这些模型虽然在正确指定时是有效的，但会做出强有力的参数假设。虽然非参数模型避免了重要的函数形式假设，但它们通常需要更大的样本才能达到给定的精度。我们进行了一项模拟研究，以评估性能之间的权衡正确指定的参数和非参数模型，以检测效果修改的二进制曝光的二进制和连续修改器。我们评估了广义线性模型和双稳健（DR）估计，有和没有样本分裂。用三次样条、分数多项式和非参数DR学习器对连续修改器进行建模。对于二元修饰剂，广义线性模型显示出最大的功效来检测效应修饰，在最坏和最好的情况下，范围分别为0.42至1.00。增强的逆概率加权具有最低的功效，当使用样本分裂时增加了23%。对于连续的修改器，DR学习器在捕获二次和非线性单调函数方面与灵活的参数模型相当。然而，对于非线性、非单调函数，DR学习器的综合偏差低于样条函数和分数多项式，分别为141.3、251.7和209.0。我们的研究结果表明，非参数和正确指定的参数模型在评估效果修改之间的性能相当。
Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification.