🤖 AI Summary
This study addresses the vulnerability of existing Bayesian dynamic borrowing methods to parametric model misspecification by proposing a nonparametric latent exchangeable prior framework. Integrating Bayesian model averaging with kernel methods, this approach enables individual-level assessment for historical data borrowing without requiring outcome model assumptions, thereby effectively mitigating triple misspecification risks while ensuring posterior consistency. Simulation studies demonstrate that the proposed method outperforms conventional parametric and semiparametric alternatives. Furthermore, its efficacy is successfully validated in a lung cancer clinical trial. Collectively, this work provides a robust nonparametric solution for dynamic information borrowing, offering significant improvements in reliability over traditional approaches when model assumptions are uncertain or violated.
📝 Abstract
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to misspecification. We propose the nonparametric latent exchangeability prior (NP-LEAP), an outcome-agnostic, assumption-lean framework to borrow information from historical data. The NP-LEAP performs individual-level exchangeability assessment, inducing Bayesian model averaging over all possible partitions of the historical data into exchangeable and nonexchangeable subsets. Although applicable to a variety of data types with choice of appropriate kernel, the NP-LEAP is particularly well-suited for studies with time-to-event outcomes, where parametric BDB is potentially triply misspecified - imposing a parametric baseline hazard, the proportional hazards structure, and blanket exchangeability. We establish posterior consistency under mild regularity conditions. Simulation studies demonstrate favorable operating characteristics relative to parametric borrowing methods and nonborrowing semiparametric frequentist methods. We illustrate the method by augmenting the control arm in a randomized trial of patients with non-small cell lung cancer.