Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
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DOI：
10.1177/0962280214521341
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发表时间：
2016-10
影响因子：
2.3
通讯作者：
Sekhon JS
中科院分区：
文献类型：
--
作者：
Kreif N;Gruber S;Radice R;Grieve R;Sekhon JS
估计治疗效果的统计方法通常使用参数回归（例如广义线性模型）对终点或倾向评分进行建模。这些模型的错误指定可能会导致参数估计有偏差。我们比较了两种结合倾向得分和端点回归的方法，通过使用机器学习方法来估计回归函数和倾向得分，可以做出较弱的建模假设。目标最大似然估计是一种双鲁棒方法，旨在减少感兴趣参数估计中的偏差。偏差校正匹配通过使用回归预测来减少由于匹配对之间的协变量不平衡而导致的偏差。我们举例说明了评估不同类型髋关节假体对骨关节炎患者健康相关生活质量的影响的方法。我们进行了一项以案例研究为基础的模拟研究，以比较这些方法的相对偏差、效率和置信区间覆盖范围。我们考虑具有非线性函数形式关系、正常和非正常端点的数据生成过程。我们发现，在考虑的情况下，偏差校正匹配通常报告的偏差较小，但方差高于目标最大似然估计。当目标最大似然估计或偏差校正匹配纳入机器学习时，与使用错误指定的参数模型相比，偏差大大减少。
Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maximum likelihood estimation is a double-robust method designed to reduce bias in the estimate of the parameter of interest. Bias-corrected matching reduces bias due to covariate imbalance between matched pairs by using regression predictions. We illustrate the methods in an evaluation of different types of hip prosthesis on the health-related quality of life of patients with osteoarthritis. We undertake a simulation study, grounded in the case study, to compare the relative bias, efficiency and confidence interval coverage of the methods. We consider data generating processes with non-linear functional form relationships, normal and non-normal endpoints. We find that across the circumstances considered, bias-corrected matching generally reported less bias, but higher variance than targeted maximum likelihood estimation. When either targeted maximum likelihood estimation or bias-corrected matching incorporated machine learning, bias was much reduced, compared to using misspecified parametric models.
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影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
DOI：
10.1007/s10742-011-0072-8
发表时间：
2011-07-01
影响因子：
1.5
作者：
Basu, Anirban;Polsky, Daniel;Manning, Willard G.
通讯作者：
Manning, Willard G.
影响因子：
5.3
作者：
Caliendo, Marco;Kopeinig, Sabine
通讯作者：
Kopeinig, Sabine
影响因子：
6.1
作者：
Abadie, A;Imbens, GW
通讯作者：
Imbens, GW