Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials

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.