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

2025-06-18

AI Summary

In hybrid control trial designs, statistical power is often compromised due to baseline covariate distribution shift between current and historical studies. To address this, we propose two blinded sample size re-estimation strategies. Innovatively, we introduce the first covariate-shift-sensitive, dynamic sample size adjustment method based on inverse probability weighting (IPW) that operates entirely under blinding—requiring no access to treatment assignment—and enables real-time adaptation to distributional changes. Through comprehensive simulation studies and validation using real-world randomized clinical trial data, our methods robustly maintain nominal statistical power (e.g., 80% or 90%), significantly outperforming conventional fixed-sample designs. Case analyses confirm their feasibility, robustness, and practical implementability. This work establishes a novel paradigm for enhancing statistical reliability in hybrid control trials.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Adjust sample size in blinded hybrid-control designs using IPW
• Address baseline covariate discrepancies between current and historical studies
• Maintain statistical power by re-estimating sample size mid-study

Innovation

• Uses inverse probability weighting for sample adjustment
• Blinded hybrid-control design with external data
• Detects baseline covariate discrepancies dynamically