Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
Department of Biometrics, Vertex Pharmaceuticals, Boston, Massachusetts, USA.
Stat Med. 2021 Dec 10;40(28):6373-6386. doi: 10.1002/sim.9188. Epub 2021 Sep 21.
In clinical trials, surrogate endpoints are useful when the endpoint of interest is difficult to measure or requires a long follow-up time. Current methodology for validating surrogate endpoints encounters challenges in the presence of collinearity between the treatment and surrogate endpoint, which is often present in clinical trials. The proposed methods adapt current methodology in the structural framework of path analysis to quantify the validity of a surrogate endpoint. The path analysis framework provides an improved interpretation of treatment effect. Through derivation and simulation we show the proposed path likelihood reduction factor (LRF ), is less biased and more robust than current methodology in cases of collinearity between the treatment and surrogate endpoint, with notable improvement when surrogacy is weak or moderate. LRF can be expanded to evaluate multiple correlated surrogate endpoints, which as shown through simulation, is also less biased and more robust than current methodology in the case of collinearity between the treatment and surrogate endpoint.
在临床试验中，当感兴趣的终点难以测量或需要长时间随访时，替代终点是有用的。目前验证替代终点的方法在治疗与替代终点之间存在共线性时会遇到挑战，这种共线性在临床试验中经常出现。所提出的方法在路径分析的结构框架中适应了当前的方法，以量化替代终点的有效性。路径分析框架提供了对治疗效果的改进解释。通过推导和模拟，我们表明，在治疗与替代终点之间存在共线性的情况下，所提出的路径似然减少因子（LRF）比当前的方法具有更小的偏差和更强的稳健性，并且在替代效果较弱或中等时，效果有显著提高。LRF 可以扩展到评估多个相关的替代终点，通过模拟表明，在治疗与替代终点之间存在共线性的情况下，它也比当前的方法具有更小的偏差和更强的稳健性。
