Jackson Dan, Zhang Fanni, Burman Carl-Fredrik, Sharples Linda
Statistical Innovation, AstraZeneca, Cambridge, UK.
Statistical Innovation, AstraZeneca, Gaithersburg, USA.
Pharm Stat. 2025 Mar-Apr;24(2):e2456. doi: 10.1002/pst.2456. Epub 2024 Nov 25.
The number of clinical trials that include a binary biomarker in design and analysis has risen due to the advent of personalised medicine. This presents challenges for medical decision makers because a drug may confer a stronger effect in the biomarker positive group, and so be approved either in this subgroup alone or in the all-comer population. We develop and evaluate Bayesian methods that can be used to assess this. All our methods are based on the same statistical model for the observed data but we propose different prior specifications to express differing degrees of knowledge about the extent to which the treatment may be more effective in one subgroup than the other. We illustrate our methods using some real examples. We also show how our methodology is useful when designing trials where the size of the biomarker negative subgroup is to be determined. We conclude that our Bayesian framework is a natural tool for making decisions, for example, whether to recommend using the treatment in the biomarker negative subgroup where the treatment is less likely to be efficacious, or determining the number of biomarker positive and negative patients to include when designing a trial.
由于个性化医疗的出现，在设计和分析中纳入二元生物标志物的临床试验数量有所增加。这给医学决策者带来了挑战，因为一种药物可能在生物标志物阳性组中产生更强的效果，因此可能仅在该亚组或所有受试者人群中获得批准。我们开发并评估了可用于评估此情况的贝叶斯方法。我们所有的方法都基于对观测数据的相同统计模型，但我们提出了不同的先验规范，以表达关于治疗在一个亚组中可能比另一个亚组更有效的程度的不同程度的知识。我们用一些实际例子来说明我们的方法。我们还展示了我们的方法在设计确定生物标志物阴性亚组规模的试验时是如何有用的。我们得出结论，我们的贝叶斯框架是做出决策的自然工具，例如，是否建议在治疗不太可能有效的生物标志物阴性亚组中使用该治疗方法，或者在设计试验时确定纳入的生物标志物阳性和阴性患者的数量。
