Quantifying statistical power gains from prognostic modeling in Huntington's Disease
- 1F. Hoffmann-La Roche Ltd
Abstract
Background and ObjectivesHuntingtons disease is a rare neurodegenerative disease in which optimizing clinical trial design is essential. Although baseline characteristics are known to predict disease progression, the statistical efficiency gains achievable by formally integrating prognostic predictions into trial analyses have not been systematically quantified. We aimed to benchmark prognostic modeling approaches, quantify the trial efficiency gains from prognostic covariate adjustment, and deliver a ready-to-deploy framework for immediate implementation.
MethodsWe developed a two-step modeling pipeline using longitudinal Enroll-HD natural history data (NCT01574053) to train nine machine learning algorithms to predict disease progression across five UHDRS endpoints. For this, we used 16 predictive baseline covariates representing stage of disease (baseline score levels), disease specific (CAP, CAG length), demographics and co-medication use that could be quantified in one clinical visit. For Total Function Capacity (TFC), frequently used as a primary endpoint in clinical trials, we also devised a minimal prognostic model based on 6 covariates. Model performance was tested using the placebo arm of the GENERATION-HD1 phase 3 trial (NCT03761849).
ResultsModels were trained on 16,398 Enroll-HD participants and tested on 260 participants from the GENERATION-HD1 placebo arm. Models that take into account nonlinear relationships between the covariates showed no performance advantage over linear-based models on the test set. For TFC, a minimal six-covariate linear prognostic model performed on par with the full 16-feature model. Adjusting for the prognostic score increased the signal-to-noise ratio across different trial visits by 8-18%, raising statistical power from 80% to up to 90% at a fixed sample size, or, equivalently, reducing required sample size by 15-28% compared to unadjusted analyses (no covariate adjustment). Compared to routinely used covariate adjustments (including baseline score, age, CAG repeat length, and CAP score), adjusting for a prognostic score consistently delivered meaningful additional efficiency gains.
DiscussionPrognostic covariate adjustment using a simple six-covariate linear score derived from a single baseline visit yields meaningful gains in statistical power for HD clinical trials, while remaining fully embedded within standard, regulator-accepted analysis frameworks. This approach offers a practical and immediately deployable tool to improve trial design.
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BibTeX
@misc{puente2026quantifying,
title = {{Quantifying statistical power gains from prognostic modeling in Huntington's Disease}},
author = {C. Noguerol Puente and F. Brizzi and A. Soubret and P. McColgan and J. Long and M. Boareto},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.26365884},
url = {https://www.medrxiv.org/content/10.64898/2026.10.19.26365884v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Quantifying statistical power gains from prognostic modeling in Huntington's Disease
AU - C. Noguerol Puente
AU - F. Brizzi
AU - A. Soubret
AU - P. McColgan
AU - J. Long
AU - M. Boareto
PY - 2026
DA - 2026/10/19
PB - medRxiv
DO - 10.64898/2026.10.19.26365884
UR - https://www.medrxiv.org/content/10.64898/2026.10.19.26365884v1
AB - Background and ObjectivesHuntingtons disease is a rare neurodegenerative disease in which optimizing clinical trial design is essential. Although baseline characteristics are known to predict disease progression, the statistical efficiency gains achievable by formally integrating prognostic predictions into trial analyses have not been systematically quantified. We aimed to benchmark prognostic modeling approaches, quantify the trial efficiency gains from prognostic covariate adjustment, and deliver a ready-to-deploy framework for immediate implementation. MethodsWe developed a two-step modeling pipeline using longitudinal Enroll-HD natural history data (NCT01574053) to train nine machine learning algorithms to predict disease progression across five UHDRS endpoints. For this, we used 16 predictive baseline covariates representing stage of disease (baseline score levels), disease specific (CAP, CAG length), demographics and co-medication use that could be quantified in one clinical visit. For Total Function Capacity (TFC), frequently used as a primary endpoint in clinical trials, we also devised a minimal prognostic model based on 6 covariates. Model performance was tested using the placebo arm of the GENERATION-HD1 phase 3 trial (NCT03761849). ResultsModels were trained on 16,398 Enroll-HD participants and tested on 260 participants from the GENERATION-HD1 placebo arm. Models that take into account nonlinear relationships between the covariates showed no performance advantage over linear-based models on the test set. For TFC, a minimal six-covariate linear prognostic model performed on par with the full 16-feature model. Adjusting for the prognostic score increased the signal-to-noise ratio across different trial visits by 8-18%, raising statistical power from 80% to up to 90% at a fixed sample size, or, equivalently, reducing required sample size by 15-28% compared to unadjusted analyses (no covariate adjustment). Compared to routinely used covariate adjustments (including baseline score, age, CAG repeat length, and CAP score), adjusting for a prognostic score consistently delivered meaningful additional efficiency gains. DiscussionPrognostic covariate adjustment using a simple six-covariate linear score derived from a single baseline visit yields meaningful gains in statistical power for HD clinical trials, while remaining fully embedded within standard, regulator-accepted analysis frameworks. This approach offers a practical and immediately deployable tool to improve trial design.
ER -