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Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment
Emilie Højbjerre-Frandsen1,2, Mark J van der Laan3, Alejandro Schuler3
1Biostatistics Methods, Novo Nordisk A/S, Søborg, Denmark.
Statistics in Medicine
|April 8, 2026
Summary
This study enhances randomized clinical trials (RCTs) by using historical data to improve statistical power and treatment effect estimation. The new method boosts efficiency without bias, aiding drug development and clinical research.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Statistical Power Analysis
Background:
- Accurate estimation of marginal treatment effects in randomized clinical trials (RCTs) is vital for intervention efficacy.
- Enhancing statistical power in RCTs is a key objective, with historical data offering potential efficiency gains.
- Existing methods for historical data borrowing often compromise type I error rate control.
Purpose of the Study:
- To extend prognostic score adjustment methodology to generalized linear models (GLMs) for plug-in analysis in RCTs.
- To enhance the efficiency and precision of marginal treatment effect estimates in RCTs by leveraging historical control data.
- To introduce a method that improves statistical power without introducing bias, building upon prior work.
Main Methods:
- Training a prognostic model on historical control data.
- Incorporating prognostic scores as covariates in a plug-in GLM analysis of trial data.
- Extending the GLM plug-in method to include Negative Binomial regression and providing a variance estimation formula.
Main Results:
- The proposed method achieves local semi-parametric efficiency under an additive treatment effect assumption.
- Demonstrated increased statistical power or reduced standard error, even without an additive treatment effect.
- Showcased reductions in estimated variance using clinical trial data, confirming theoretical benefits.
Conclusions:
- The enhanced GLM plug-in method effectively utilizes historical data to improve RCT efficiency and precision.
- This approach offers a statistically sound way to increase power in clinical trials without compromising bias control.
- The findings have implications for optimizing trial design and analysis, particularly in pharmaceutical research.
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