Pierce Matthias, Emsley Richard
Centre for Biostatistics, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, 1st Floor, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL, UK.
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Trials. 2021 Jan 6;22(1):20. doi: 10.1186/s13063-020-04901-2.
In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem.
We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer's approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers.
The regression approach outperformed Kraemer's approach across all data-generating scenarios. The modification of Kraemer's approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm.
The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.
在存在异质性治疗效果的情况下，期望根据患者对治疗的预期反应将其分为亚组。这通过个性化治疗推荐得以形式化：一种使用生物标志物测量值来选择治疗方法的算法。可能多个而非单个生物标志物能更好地预测这些亚组。然而，找到多个生物标志物的最佳组合可能是一个困难的预测问题。
我们描述了三种使用随机试验数据在个性化治疗推荐中找到生物标志物最佳组合的参数方法：一种回归方法，通过治疗与生物标志物的相互作用对结果进行建模；一种由克雷默提出的方法，该方法根据所有治疗组和对照组对中计算出的各个生物标志物权重形成一个综合指标；以及对克雷默方法的一种新颖改进，该改进利用预后评分对匹配的治疗组和对照组受试者进行抽样。在多个数据生成模型下使用蒙特卡罗模拟，我们比较这些方法，并根据与标准治疗相比在个性化治疗推荐下的改善程度来得出结论。这三种方法应用于一项针对慢性疲劳综合征患者的家庭实用康复与常规治疗随机试验的数据（FINE试验）。对这些数据的先前分析表明，多个相关生物标志物存在一些治疗效果异质性。
在所有数据生成场景中，回归方法均优于克雷默的方法。对克雷默方法的改进导致了更好的治疗推荐，除非存在一个强大的未观察到的预后生物标志物。在FINE示例中，回归方法在其个性化治疗推荐算法下显示出微弱的改善。
克雷默提出的方法在组合多个生物标志物方面并不比回归方法表现更好。所有方法对参数模型的错误设定都很敏感。
