Leveraging Covariates for Sensitivity Analysis of Hybrid Control Trials

Music City Center 

In the digital era it is easier than ever to collect and exploit rich covariate information in trials. Recent work explores how to use this information to integrate external controls, including the use of hybrid control trials (HCTs) where a randomized controlled trial is augmented with external controls. HCTs are of particular interest due to their ability to preserve partial randomization while also improving trial efficiency.
However, most HCT estimators rely on an unrealistic assumption: mean exchangeability of the controls. Literature has focused on the development of these estimators, but few, if any, have discussed a formal approach to address the inevitable bias introduced from a violation of this assumption, slowing the acceptance of this study design.
To address this, we introduce a non-parametric sensitivity analysis that recognizes that the assumption can be reframed as a 'no unobserved confounders' assumption. We leverage omitted variable bias methodologies to estimate the maximum bias introduced from unmeasured covariates, allowing for a critical evaluation of the causal gap that invalidates significant findings. We show that with sufficient understanding of the covariate-outcome relationship, this method reliably bounds bias while also allowing for gains in efficiency.
By enhancing the credibility of HCT findings, our method aims to facilitate the broader adoption of HCTs, thereby safely accelerating the development of new therapies, especially in underfunded or underrepresented areas of research.

External Data

Hybrid Control Trial

Covariates

Sensitivity Analysis

Historical Controls

Unobserved Confounding