Given the heterogeneous responses to therapy and the high cost of treatments, there is an increasing interest in identifying pretreatment predictors of therapeutic effect. Clearly, the success of such an endeavor will depend on the amount of information that the patient-specific variables convey about the individual causal treatment effect on the response of interest. In the present work, using causal inference and information theory, a strategy is proposed to evaluate individual predictive factors for cancer immunotherapy efficacy. In a first step, the methodology proposes a causal inference model to describe the joint distribution of the pretreatment predictors and the individual causal treatment effect. Further, in a second step, the so-called predictive causal information (PCI), a metric that quantifies the amount of information the pretreatment predictors convey on the individual causal treatment effects, is introduced and its properties are studied. The methodology is applied to identify predictors of therapeutic success for a therapeutic vaccine in advanced lung cancer. A user-friendly R library EffectTreat is provided to carry out the necessary calculations.

中文翻译：

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确定治疗效果的个体预测因素

鉴于对治疗的异质反应和高昂的治疗成本，人们越来越关注识别治疗效果的预处理预测因子。显然，这种努力的成功将取决于患者特定变量传达的有关个体因果治疗对感兴趣反应的影响的信息量。在目前的工作中，使用因果推理和信息论，提出了一种评估癌症免疫治疗效果的个体预测因素的策略。在第一步中，该方法提出了一个因果推理模型来描述预处理预测因子的联合分布和个体因果治疗效果。此外，在第二步中，所谓的预测因果信息 (PCI)，引入了一个度量，该度量量化了预处理预测器传达的关于个体因果治疗效果的信息量，并研究了它的特性。该方法用于确定晚期肺癌治疗性疫苗治疗成功的预测因子。一个用户友好的 R 库提供EffectTreat以进行必要的计算。