论文标题
增强算法来估计最佳的个性化治疗规则
Boosting Algorithms for Estimating Optimal Individualized Treatment Rules
论文作者
论文摘要
我们提出了用于估计最佳个性化治疗规则的非参数算法。所提出的算法基于XGBoost算法，该算法被称为机器学习文献中最强大的算法之一。我们的主要思想是通过加性回归树对临床结果或决策规则的条件平均值进行建模，并使用增强技术估算每棵树的迭代。我们的方法克服了正确模型规范的挑战，这是当前参数方法所必需的。我们提出的算法的主要贡献是对经常在实践中经常出现的高度非线性和复杂最佳的个性化治疗规则进行有效，准确的估计。最后，我们通过广泛的仿真研究说明了算法的出色性能，并以对糖尿病III期试验的真实数据的应用结论。
We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature. Our main idea is to model the conditional mean of clinical outcome or the decision rule via additive regression trees, and use the boosting technique to estimate each single tree iteratively. Our approaches overcome the challenge of correct model specification, which is required in current parametric methods. The major contribution of our proposed algorithms is providing efficient and accurate estimation of the highly nonlinear and complex optimal individualized treatment rules that often arise in practice. Finally, we illustrate the superior performance of our algorithms by extensive simulation studies and conclude with an application to the real data from a diabetes Phase III trial.
