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A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS

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Using principal progression rate to quantify and compare disease progression in comparative studies.

Changyu Shen¹, Menglan Pang¹, Ling Zhu¹

¹Statistical Sciences & Evidence Generation, Biogen, Cambridge, MA, USA.

Journal of Biopharmaceutical Statistics

|May 8, 2026

Summary

Principal Progression Rate (PPR) offers a more powerful way to analyze longitudinal data in clinical trials compared to the standard mean Change From Baseline (CFB). PPR enhances statistical power by better capturing treatment effects in progressive diseases.

Keywords:

Change from baseline Principal Progression Rate effect size estimation precision statistical power

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Area of Science:

Biostatistics
Clinical Trial Methodology
Longitudinal Data Analysis

Background:

Standard analysis of progressive diseases in clinical trials uses mean Change From Baseline (CFB), which may not fully utilize longitudinal data or capture treatment effect nuances.
Mean CFB estimation does not leverage the entire outcome trajectory, potentially limiting statistical power and precision in evaluating treatment efficacy.

Purpose of the Study:

To introduce a novel class of estimands, Principal Progression Rate (PPR), designed to improve the analysis of longitudinal data in progressive disease studies.
To demonstrate that PPR can offer enhanced statistical power and precision compared to the traditional mean CFB estimand.

Main Methods:

Proposed Principal Progression Rate (PPR) as a weighted average of the instantaneous slope of the mean outcome trajectory.
Showcased the flexibility of PPR's weight function, encompassing existing estimands like mean CFB and ordinary least-squares slope.
Evaluated PPR estimators through numerical simulations and application to the EMERGE clinical trial dataset.

Main Results:

Demonstrated that properly selected PPRs can significantly enhance statistical power over mean CFB.
PPR amplifies treatment effect signals and improves estimation precision by utilizing more information from the longitudinal data.
Numerical studies and the EMERGE trial analysis confirmed the advantages of using PPR over mean CFB.

Conclusions:

Principal Progression Rate (PPR) provides a more efficient and powerful approach for analyzing longitudinal data in progressive disease clinical trials.
PPR offers a flexible framework that can be tailored to specific study objectives, outperforming traditional mean CFB analysis.
The findings suggest that PPR should be considered as a valuable alternative estimand for evaluating treatment effects in longitudinal studies.