Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine
Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine
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比较因果森林和基于回归的方法来评估治疗效果异质性：2 型糖尿病精准医疗的应用
DOI：
10.1101/2022.11.07.22282023
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Venkatasubramaniam A
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文献类型：
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作者：
Venkatasubramaniam A
精准医学需要可靠地识别不同可用治疗的患者水平结果的变化，通常称为治疗效果异质性。我们的目的是评估比较效用的个性化治疗选择策略的基础上预测的个人水平的治疗效果从因果森林机器学习算法和惩罚回归model.MethodsCohort研究特征的个人水平的降糖反应（6个月减少HbA 1c）在2型糖尿病患者启动SGLT 2抑制剂或DPP 4抑制剂治疗。模型开发集包括SGLT 2抑制剂与DPP 4抑制剂的CANTATA-D和CANTATA-D2随机临床试验的1，428例受试者。对于外部验证，在英国初级保健的18，741例患者中评价了根据预测HbA 1c获益的大小定义的患者分层中HbA 1c观察值与预测值差异的校准结果在两种方法的临床试验参与者中检测到治疗效果的异质性（预计SGLT 2抑制剂治疗优于DPP 4抑制剂治疗的比例：因果森林：98.6%;惩罚回归：81.7%）。在验证中，惩罚回归的校准效果良好，但因果森林的校准效果不佳。使用SGLT 2抑制剂时HbA 1c获益> 10 mmol/mol的分层（3.7%的患者，观察到的获益11.0 mmol/mol [95%CI 8.0-14.0]）使用惩罚回归而非因果森林确定，并且使用惩罚回归确定了SGLT 2抑制剂HbA 1c获益5-10 mmol的更大分层（回归：20.9%的患者，观察到获益7.8 mmol/mol（95%CI 6.7-8.9）;因果林11.6%，观测效益8.7 mmol/mol（95%CI 7.4-10.1）。结论与近期临床资料预测结果一致，当评估治疗效果异质性时，研究人员不应单独依赖因果森林或其他类似的机器学习算法，而必须将输出与标准回归进行比较，在该评估中，标准回归是上级的。
ObjectivePrecision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.MethodsCohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink).ResultsHeterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0–14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5–10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7–8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4–10.1).ConclusionsConsistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.