Lamont Andrea, Lyons Michael D, Jaki Thomas, Stuart Elizabeth, Feaster Daniel J, Tharmaratnam Kukatharmini, Oberski Daniel, Ishwaran Hemant, Wilson Dawn K, Van Horn M Lee
1 Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA.
2 Department of Psychology, University of Houston, Houston, USA.
Stat Methods Med Res. 2018 Jan;27(1):142-157. doi: 10.1177/0962280215623981. Epub 2016 Mar 17.
In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.
在大多数医学研究中，治疗效果是通过平均治疗效果或某种形式的亚组分析来评估的。然而，个性化或精准医学的实践需要新的方法来预测个体对治疗的反应，而不是依赖于总体效果指标。在本研究中，我们提出了一个用于估计个体治疗效果的概念框架，称为预测个体治疗效果。我们首先将预测个体治疗效果方法应用于一项旨在改善行为和身体症状的随机对照试验。尽管干预的平均效果微不足道，但我们使用预测个体治疗效果方法显示出预测个体治疗反应存在显著的异质性。预测个体治疗效果可用于预测干预可能最有效（或有害）的个体。接下来，我们进行了一项蒙特卡洛模拟研究，以评估预测个体治疗效果的准确性。我们比较了用于获得预测的两种方法的性能：多重插补和非参数随机决策树。结果表明，平均而言，两种预测方法在个体水平上都产生了准确的估计；然而，随机决策树往往会低估极端人群的预测个体治疗效果，并且与插补方法相比，在重复预测中显示出更大的变异性。我们还讨论了局限性和未来方向。
