Improving clinical trial efficiency with machine learning models of disease progression

Beaulieu, Danielle; Taylor, Albert A.; Pierce, Dustin; Cuerdo, Jonavelle; Schactman, Mark; Keymer, Mike; Ennist, David L.

doi:10.1016/b978-0-12-816475-4.00005-7

Cited by 1 publication

(2 citation statements)

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“…This analysis utilized a previously validated gradient boosting machine model to predict the log-hazard risk of patients progressing to 50% expected vital capacity (VC50) . This model was developed using the clinical trial records of 4600 unique patients included in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database (the "internal" data set) (14). VC50 was reached during the period of their respective trials by 1926 (42%) of the patients.…”

Section: Machine Learning Modelmentioning

confidence: 99%

“…VC50 was reached during the period of their respective trials by 1926 (42%) of the patients. The model has an average area under the receiver operating curve (AUC) measured at the 1-year time point in a 10-fold internal cross-validation of 0.868, and an AUC of 0.923 using the placebo arm of the contemporary, external 6-month-long BENEFIT-ALS clinical trial (14,15).…”

Section: Machine Learning Modelmentioning

confidence: 99%

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Evidence for generalizability of edaravone efficacy using a novel machine learning risk-based subgroup analysis tool

Brooks¹,

Pioro

Beaulieu

et al. 2021

Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration

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Introduction: The edaravone development program for amyotrophic lateral sclerosis (ALS) included trials MCI186-16 (Study 16) and . A cohort enrichment strategy was based on a Study 16 post hoc analysis and applied to Study 19 to elucidate a treatment effect in that study. To determine whether the Study 19 results could be generalized to a broader ALS population, we used a machine learning (ML) model to create a novel risk-based subgroup analysis tool. Methods: A validated ML model was used to rank order all Study 16 participants by predicted time to 50% expected vital capacity. Subjects were stratified into nearest-neighbor risk-based subgroups that were systematically expanded to include the entire Study 16 population. For each subgroup, a statistical analysis generated heat maps that revealed statistically significant effect sizes. Results: A broad region of the Study 16 heat map with significant effect sizes was identified, including up to 70% of the trial population. Incorporating participants identified in the cohort enrichment strategy yielded a broad group comprising 76% of the original participants with a statistically significant treatment effect. This broad group spanned the full range of the functional score progression observed in Study 16. Conclusions: This analysis, applying predictions derived using an ML model to a novel methodology for subgroup identification, ascertained a statistically significant edaravone treatment effect in a cohort of participants with broader disease characteristics than the Study 19 inclusion criteria. This novel methodology may assist clinical interpretation of study results and potentially inform efficient future clinical trial design strategies.

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Section: Machine Learning Modelmentioning

confidence: 99%

Section: Machine Learning Modelmentioning

confidence: 99%