Unmasking Baseline Inflation In Clinical Trials: A Critical But Addressable Challenge

By Marcela Roy, Sayaka Machizawa, Gary Sachs, and Alan Kott

Baseline inflation—the systematic overestimation of symptom severity at study entry—poses a major threat to CNS trial integrity by constraining the measurable range of improvement and obscuring true treatment effects. This bias stems from convergent pressures: sites striving to meet enrollment goals, investigators motivated to help patients access treatment, and participants motivated to qualify. Compounding these human dynamics, rating scales with ambiguous anchors and single-timepoint eligibility thresholds further encourage inflated assessments. As a result, many trials enroll participants who appear more impaired than they are, fundamentally weakening assay sensitivity and drug–placebo separation.

To address this, we developed a simulation-based calibration method using virtual raters—algorithm-driven, standardized assessments validated against expert consensus. These benchmarks reveal discrepancies in site-based ratings, with inflation correlating strongly to attenuated efficacy signals. Prospective use enables targeted intervention before randomization, preserving study integrity. When combined with machine learning–driven quality monitoring and optimized protocol designs, virtual raters offer a scalable solution to baseline inflation, marking a pivotal advance in ensuring data integrity and accelerating therapeutic development.