Sample size re-estimation in blinded hybrid-control design using inverse probability weighting

TL;DR

This paper introduces two blinded sample size re-estimation methods using inverse probability weighting to adaptively increase sample size in hybrid control trials when baseline covariate differences threaten statistical power.

Contribution

It proposes novel IPW-based strategies for blinded sample size adjustment in hybrid control designs, addressing covariate imbalance issues during the trial.

Findings

Strategies effectively maintain power in simulations.

Methods successfully applied in real clinical study case.

Sample size adjustments improve trial robustness.

Abstract

With the increasing availability of data from historical studies and real-world data sources, hybrid control designs that incorporate external data into the evaluation of current studies are being increasingly adopted. In these designs, it is necessary to pre-specify during the planning phase the extent to which information will be borrowed from historical control data. However, if substantial differences in baseline covariate distributions between the current and historical studies are identified at the final analysis, the amount of effective borrowing may be limited, potentially resulting in lower actual power than originally targeted. In this paper, we propose two sample size re-estimation strategies that can be applied during the course of the blinded current study. Both strategies utilize inverse probability weighting (IPW) based on the probability of assignment to either the current or historical study. When large discrepancies in baseline covariates are detected, the proposed strategies adjust the sample size upward to prevent a loss of statistical power. The performance of the proposed strategies is evaluated through simulation studies, and their practical implementation is demonstrated using a case study based on two actual randomized clinical studies.