Trenou Kossi Clément, Mésidor Miceline, Eslami Aida, Nabi Hermann, Diorio Caroline, Talbot Denis
Département de médecine sociale et préventive, Université Laval, Québec, Canada.
Axe santé des populations et pratiques optimales en santé, Centre de recherche du CHU de Québec - Université Laval, Québec, Canada.
Biom J. 2025 Aug;67(4):e70068. doi: 10.1002/bimj.70068.
Estimating optimal adaptive treatment strategies (ATSs) can be done in several ways, including dynamic weighted ordinary least squares (dWOLS). This approach is doubly robust as it requires modeling both the treatment and the response, but only one of those models needs to be correctly specified to obtain a consistent estimator. For estimating an average treatment effect, doubly robust methods have been shown to combine better with machine learning methods than alternatives. However, the use of machine learning within dWOLS has not yet been investigated. Using simulation studies, we evaluate and compare the performance of the dWOLS estimator when the treatment probability is estimated either using machine learning algorithms or a logistic regression model. We further investigate the use of an adaptive -out-of- bootstrap method for producing inferences. SuperLearner performed at least as well as logistic regression in terms of bias and variance in scenarios with simple data-generating models and often had improved performance in more complex scenarios. Moreover, the -out-of- bootstrap produced confidence intervals with nominal coverage probabilities for parameters that were estimated with low bias. We also apply our proposed approach to the data from a breast cancer registry in Québec, Canada, to estimate an optimal ATS to personalize the use of hormonal therapy in breast cancer patients. Our method is implemented in the R software and available on GitHub https://github.com/kosstre20/MachineLearningToControlConfoundingPersonalizedMedicine.git. We recommend routine use of machine learning to model treatment within dWOLS, at least as a sensitivity analysis for the point estimates.
估计最佳适应性治疗策略（ATSs）有多种方法，包括动态加权普通最小二乘法（dWOLS）。这种方法具有双重稳健性，因为它需要对治疗和反应进行建模，但只需要正确指定其中一个模型就能获得一致的估计量。对于估计平均治疗效果，已证明双重稳健方法比其他方法更能与机器学习方法相结合。然而，尚未研究在dWOLS中使用机器学习的情况。通过模拟研究，我们评估并比较了在使用机器学习算法或逻辑回归模型估计治疗概率时dWOLS估计量的性能。我们进一步研究了使用自适应的k折自助法进行推断。在具有简单数据生成模型的场景中，SuperLearner在偏差和方差方面的表现至少与逻辑回归一样好，并且在更复杂的场景中通常具有更好的性能。此外，k折自助法为低偏差估计的参数产生了具有名义覆盖概率的置信区间。我们还将我们提出的方法应用于加拿大魁北克省乳腺癌登记处的数据，以估计一种最佳的ATS，用于个性化乳腺癌患者激素治疗的使用。我们的方法在R软件中实现，可在GitHub上获取：https://github.com/kosstre20/MachineLearningToControlConfoundingPersonalizedMedicine.git。我们建议在dWOLS中常规使用机器学习对治疗进行建模，至少作为点估计的敏感性分析。
