ArticleAnnals of clinical and translational neurology2020
Does including machine learning predictions in ALS clinical trial analysis improve statistical power?
Article in Annals of clinical and translational neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Multi-scale machine learning model predicts muscle and functional disease progression.Scientific reports · 2025Article
- FVC-DiP correlates with neurofilament light chain levels in serum and cerebrospinal fluid in patients with ALS.BMJ neurology open · 2025Article
- Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.Annals of clinical and translational neurology · 2023Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- Considerations for Amyotrophic Lateral Sclerosis (ALS) Clinical Trial Design.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing predictive models for disease progression. However, the utility of predictive modeling on clinical trial analysis has not been thoroughly evaluated.
methodsWe evaluated a predictive modeling approach for ALS disease progression measured by ALSFRS-R using the PRO-ACT database and validated our findings in a novel test set from a former clinical trial. We examined clinical trial scenarios where model predictions could improve statistical power for detecting treatment effects with simulated clinical trials.
resultsModels constructed with imputed PRO-ACT data have better external validation results than those fitted with complete observations. When fitted with imputed data, super learner (R
interpretationPredictive modeling approaches for ALSFRS-R are able to explain a moderate amount of variability in longitudinal change, which is improved by robust missing data handling for baseline characteristics. Including ALSFRS-R post-baseline model prediction results as a covariate in the model for primary analysis may increase power under moderate treatment effects.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.