A surrogate endpoint S in a clinical trial is an outcome that may be measured earlier or more easily than the true outcome of interest T. In this work, we extend causal inference approaches to validate such a surrogate using potential outcomes. The causal association paradigm assesses the relationship of the treatment effect on the surrogate with the treatment effect on the true endpoint. Using the principal surrogacy criteria, we utilize the joint conditional distribution of the potential outcomes T, given the potential outcomes S. In particular, our setting of interest allows us to assume the surrogate under the placebo, S(0), is zero-valued, and we incorporate baseline covariates in the setting of normally-distributed endpoints. We develop Bayesian methods to incorporate conditional independence and other modeling assumptions and explore their impact on the assessment of surrogacy. We demonstrate our approach via simulation and data that mimics an ongoing study of a muscular dystrophy gene therapy.

翻译：临床试验中的代理端点 S 是一个比真正的利益结果更早或更容易衡量的结果。 在这项工作中,我们推广因果推断方法,利用潜在结果来验证这种替代结果。因果关联模式评估替代点的治疗效应与真正终点的治疗效应之间的关系。我们使用主要代孕标准,利用潜在结果T的有条件联合分配,考虑到潜在结果S。特别是,我们的兴趣背景使我们能够在安慰剂(S(0))下承担代孕,而S(0)是零值的,我们在通常分配的端点设置中采用基线共变法。我们开发了贝叶斯方法,以纳入有条件的独立和其他模型假设,并探索其对代孕评估的影响。我们通过模拟和数据展示了我们的方法,模拟了对肌肉萎缩基因疗法进行的持续研究。