Sensitivity Analysis Framework for Unmeasured Confounding when Integrating External Controls in Randomized Controlled Trials

Oregon Convention Center 

Integrating external control subjects with randomized controlled trials (RCTs) can offer increased efficiency to estimate causal treatment effects and can overcome certain limitations of traditional RCTs, but may also be subject to potential biases and inflated type I error due to heterogeneity between the data sources. To mitigate bias and better control type I error rates, dynamic borrowing approaches have been proposed that leverage similarity between baseline or outcome variables between concurrent and external controls to determine the degree of borrowing. These have been shown to result in better bias control and can result in greater power when comparison functions are correctly specified. However, misspecification due to unmeasured confounding may lead to underestimating the degree of data heterogeneity and potential over-borrowing. To evaluate the robustness of statistical inference using external control data, we propose a fully Bayesian sensitivity analysis framework to evaluate the potential impact of unmeasured confounding under the Bayesian power prior with subject-specific weights.