  Surrogate endpoint (SE) for overall survival in cancer patients is essential
to improving the efficiency of oncology drug development. In practice, we may
discover a new patient level association with survival, based on one or more
clinical or biological features, in a discovery cohort; and then measure the
trial level association across studies in a meta-analysis to validate the SE.
To understand how well various patient level metrics would indicate the
eventual trial level association, we considered causal biological trajectories
based on bi-exponential functions, modeled the strength of their impact on
survival hazards via a parameter {\alpha}, and simulated the trajectories and
survival times in randomized trials simultaneously. We set an early time point
in the trials when the trajectory measurement became the SE value. From
simulated discovery cohorts, we compared patient level metrics including C
index, integrated brier score, and log hazard ratio between SE values and
survival times. We assembled multiple simulated studies to enable meta-analyses
to estimate the trial level association. Across all the simulation scenarios
considered here, we found tight correlations among the three patient level
metrics and similar correlations between any of them and the trial level
metric. Despite the continual increase in {\alpha}, both patient and trial
level metrics often plateaued together; their association always decreased
quickly as {\alpha} increased. This suggests that incorporating additional
biological factors into a composite SE is likely to have diminishing returns on
improving both patient level and trial level association.
