Kulasekera K B, Tholkage Sudaraka, Kong Maiying
Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA.
J Appl Stat. 2022 Jan 5;50(5):1115-1127. doi: 10.1080/02664763.2021.2019689. eCollection 2023.
Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the 'best' outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
基于个体患者特征估计最佳治疗方案一直是许多论坛讨论的话题。在过去二十年中，先进的计算能力为这一讨论增添了动力，从业者一直在倡导使用新方法来确定最佳治疗方案。根据患者的基因标记和特征制定的旨在实现“最佳”治疗效果的治疗方法至关重要。在本文中，我们开发了一种基于观察数据预测最佳个性化治疗方案的方法。我们使用治疗加权逆概率机器学习方法来获得预测最佳治疗方案的评分函数。广泛的模拟研究表明，我们提出的方法在选择最佳治疗方案方面具有理想的性能。我们提供了一个案例研究，以检验他汀类药物对认知功能的影响，来说明我们提出的方法的应用。
