Accelerated Stability Studies: A Frequentist-Bayesian Hybrid Simulation Approach Implemented in the AccelStab R Package.

Stability assessment of vaccine drug products typically requires years of data collection. Accelerated stability studies offer a practical alternative by leveraging high-temperature data collected over shorter time periods. We model degradation using arbitrary nth order kinetics and derive a closed-form solution that enables extrapolation across time and temperature. We compare interval estimation methods including the delta method, bootstrap resampling, and Bayesian inference and propose a frequentist-Bayesian hybrid (FBH) posterior approximation framework based on a multivariate Student's t-distribution. FBH is closely related to objective Bayesian inference under noninformative priors in simple Gaussian settings and fully propagates uncertainty in both model parameters and residual variance. Simulation results show that FBH outperforms standard approaches by consistently achieving nominal coverage, particularly in small-sample settings where competing methods exhibit undercoverage. The approach provides well-calibrated confidence and prediction intervals while maintaining computational efficiency comparable to analytical methods and avoiding the cost of full posterior sampling. The methodology is illustrated using two vaccine development case studies and implemented in the AccelStab R package.