Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data.

When multiple historical controls are available, it is necessary to consider the conflicts between current and historical controls and the relationships among historical controls. One of the assumptions concerning the relationships between the parameters of interest of current and historical controls is known as the "Potential biases." Within the "Potential biases" assumption, the differences between the parameters of interest of the current control and of each historical control are defined as "potential bias parameters.

Bayesian adaptive randomization design incorporating propensity score-matched historical controls.

Incorporating historical control data to augment the control arm in randomized controlled trials (RCTs) is one way of increasing their efficiency and feasibility when adequate RCTs cannot be conducted. In recent work, a Bayesian adaptive randomization design incorporating historical control data has been proposed to reduce sample size according to the amount of information that could be borrowed, assessed at interim assessment in respect to prior-data conflict. However, the approach does not distinguish between the two sources of prior-data conflict: (1) imbalance in measured covariates, and (2) imbalance in unmeasured covariates.