Treatment benefit predictors (TBPs) map patient characteristics into an estimate of the treatment benefit tailored to individual patients, which can support optimizing treatment decisions. However, the assessment of their performance might be challenging with the non-random treatment assignment. This study conducts a conceptual analysis, which can be applied to finite-sample studies. We present a framework for evaluating TBPs using observational data from a target population of interest. We then explore the impact of confounding bias on TBP evaluation using measures of discrimination and calibration, which are the moderate calibration and the concentration of the benefit index ($C_b$), respectively. We illustrate that failure to control for confounding can lead to misleading values of performance metrics and establish how the confounding bias propagates to an evaluation bias to quantify the explicit bias for the performance metrics. These findings underscore the necessity of accounting for confounding factors when evaluating TBPs, ensuring more reliable and contextually appropriate treatment decisions.

翻译：治疗获益预测模型（TBPs）将患者特征映射为针对个体患者的治疗获益估计，从而有助于优化治疗决策。然而，由于治疗分配的非随机性，其性能评估可能面临挑战。本研究进行了可应用于有限样本研究的概念性分析。我们提出了一个利用目标人群观察性数据评估TBPs的框架。随后，我们分别通过判别能力和校准度指标——即适度校准与获益集中指数（$C_b$）——探讨了混杂偏误对TBP评估的影响。我们证明了未能控制混杂因素可能导致性能指标产生误导性数值，并阐明了混杂偏误如何传导至评估偏误，从而量化性能指标的显性偏误。这些发现强调了在评估TBPs时考虑混杂因素的必要性，以确保治疗决策更可靠且更符合临床情境。