Assessing the Role of Model Complexity in Virtual Clinical Trial Outcomes

Virtual clinical trials (VCTs) hold significant promise for improving the drug development process, yet their predictive reliability depends critically on design decisions that remain poorly understood. This study examines how model complexity influences VCT outcomes, as well as how the choice of prior parameter distributions and virtual patient inclusion criteria affects those outcomes. Using oncolytic virotherapy treatment of murine tumors as a case study, we compared a relative hierarchy of three mathematical models of increasing complexity under different parameter priors (uniform and normal distributions) and two inclusion methods (accept-or-reject and accept-or-perturb). Our results demonstrate that the simplest model produces a plausible population that inadequately spans the feasible trajectory space, potentially missing critical inter-patient heterogeneity. However, we found diminishing returns beyond intermediate model complexity, as both the intermediate and complex models captured similar ranges of patient responses across dosing protocols. Notably, the accept-or-reject method generated posterior parameter distributions that are more likely to resemble the chosen prior, possibly overly reducing inter-patient variability in treatment responses, particularly at high doses. In contrast, the accept-or-perturb inclusion criteria produced more robust results that were less sensitive to prior assumptions. These findings suggest that VCT design should prioritize models with sufficient biological detail to capture key mechanisms without unnecessary complexity, paired with inclusion criteria that avoid over-constraining plausible populations to match potentially unrealistic prior assumptions.