Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.
Biometrics. 2023 Dec;79(4):2830-2842. doi: 10.1111/biom.13864. Epub 2023 Apr 17.
Multistate process data are common in studies of chronic diseases such as cancer. These data are ideal for precision medicine purposes as they can be leveraged to improve more refined health outcomes, compared to standard survival outcomes, as well as incorporate patient preferences regarding quantity versus quality of life. However, there are currently no methods for the estimation of optimal individualized treatment rules with such data. In this paper, we propose a nonparametric outcome weighted learning approach for this problem in randomized clinical trial settings. The theoretical properties of the proposed methods, including Fisher consistency and asymptotic normality of the estimated expected outcome under the estimated optimal individualized treatment rule, are rigorously established. A consistent closed-form variance estimator is provided and methodology for the calculation of simultaneous confidence intervals is proposed. Simulation studies show that the proposed methodology and inference procedures work well even with small-sample sizes and high rates of right censoring. The methodology is illustrated using data from a randomized clinical trial on the treatment of metastatic squamous-cell carcinoma of the head and neck.
多状态过程数据在癌症等慢性病的研究中很常见。与标准生存结果相比，这些数据非常适合精准医学目的，因为它们可以利用来改善更精细的健康结果，并结合患者对生活质量的数量与质量的偏好。然而，目前还没有针对此类数据的最佳个体化治疗规则估计的方法。在本文中，我们针对随机临床试验环境中的这一问题提出了一种非参数结果加权学习方法。所提出方法的理论性质，包括在估计的最佳个体化治疗规则下估计的预期结果的 Fisher 一致性和渐近正态性，都得到了严格的建立。提供了一致的闭式方差估计量，并提出了计算同时置信区间的方法。模拟研究表明，即使在小样本量和高右删失率的情况下，所提出的方法和推断程序也能很好地工作。该方法通过对头颈部转移性鳞状细胞癌治疗的随机临床试验数据进行了说明。
