用于精准医学的预测性生物标志物图形化方法（PRIME）
Predictive biomarker graphical approach (PRIME) for Precision medicine
AI总结：
本文提出PRIME方法，通过预测风险连续评估生物标志物并图形化确定截断点，扩展TreatmentSelection以纳入协变量，为精准医学提供灵活的患者富集决策工具。
摘要：
精准医学是医学领域中一个不断发展的方向，它依赖生物标志物来做出患者富集决策，从而为药物开发提供方向。传统的统计方法是寻找一个截断点，使得在该截断点处二分化后的生物标志物与治疗之间的交互作用具有最小p值。这种方法未纳入临床意义，且生物标志物未在连续尺度上进行评估。我们提出基于包含生物标志物与治疗交互作用的模型，从预测风险的角度以连续方式评估生物标志物。预测风险可以通过图形化方式展示，以解释结局与生物标志物之间的关系，从而为生物标志物阳性/阴性分组建议截断点。我们采用TreatmentSelection方法，并通过G-computation将其扩展以纳入协变量。其他特性包括使用净增益汇总指标进行生物标志物比较，以及通过校准评估模型拟合。PRIME（预测性生物标志物图形化方法）在结局类型和所考虑的协变量方面具有灵活性。已提供R包，并将演示示例。
英文摘要：
Precision medicine is an evolving area in the medical field and rely on biomarkers to make patient enrichment decisions, thereby providing drug development direction. A traditional statistical approach is to find the cut-off that leads to the minimum p-value of the interaction between the biomarker dichotomized at that cut-off and treatment. Such an approach does not incorporate clinical significance and the biomarker is not evaluated on a continuous scale. We are proposing to evaluate the biomarker in a continuous manner from a predicted risk standpoint, based on the model that includes the interaction between the biomarker and treatment. The predicted risk can be graphically displayed to explain the relationship between the outcome and biomarker, whereby suggesting a cut-off for biomarker positive/negative groups. We adapt the TreatmentSelection approach and extend it to account for covariates via G-computation. Other features include biomarker comparisons using net gain summary measures and calibration to assess the model fit. The PRIME (Predictive biomarker graphical approach) approach is flexible in the type of outcome and covariates considered. A R package is available and examples will be demonstrated.
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