Spline-Based Weighted Methods for Continuous Outcomes in External Comparator Studies

Music City Center 

External control arms (ECAs) using real-world data (RWD) offer an alternative to randomized controlled trials for assessing efficacy. Common in single-arm oncology trials with binary or time-to-event outcomes, ECAs face challenges with continuous outcomes due to differences in outcome data collection timing and frequency between clinical trials and real-world settings. We propose an innovative spline-based weighted approach to estimate the treatment effect in these ECA studies, addressing biases introduced by these differences.
Our method adjusts the ECA to align with clinical trial data through propensity score-based weighting, incorporated into a Bayesian spline model to estimate treatment effects. This approach effectively handles the irregular timing of RWD outcome collection, facilitating comparisons with scheduled trial assessments. We performed simulations to compare our approach to linear interpolation and the window approach, exploring various scenarios with different outcome distributions, levels of confounding, and degrees of irregularity in data collection. Our results demonstrate the utility of this method to minimize bias in the context of an ECA study.

External Control Arm

Continuous Outcomes

Spline Regression

Propensity score

Bayesian modeling

Real-world Evidence 

Biopharmaceutical Section