Open AccessImproved stratification of ALS clinical trials using predicted survival
Author(s) -
Berry James D.,
Taylor Albert A.,
Beaulieu Danielle,
Meng Lisa,
Bian Amy,
Andrews Jinsy,
Keymer Mike,
Ennist David L.,
Ravina Bernard
Publication year - 2018
Publication title -
annals of clinical and translational neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.824
H-Index - 42
ISSN - 2328-9503
DOI - 10.1002/acn3.550
Subject(s) - randomization , medicine , sample size determination , clinical trial , confounding , clinical endpoint , stratification (seeds) , placebo , randomized controlled trial , risk stratification , survival analysis , proportional hazards model , statistics , mathematics , pathology , seed dormancy , botany , germination , alternative medicine , dormancy , biology
In small trials, randomization can fail, leading to differences in patient characteristics across treatment arms, a risk that can be reduced by stratifying using key confounders. In ALS trials, riluzole use ( RU ) and bulbar onset ( BO ) have been used for stratification. We hypothesized that randomization could be improved by using a multifactorial prognostic score of predicted survival as a single stratifier. Methods We defined a randomization failure as a significant difference between treatment arms on a characteristic. We compared randomization failure rates when stratifying for RU and BO (“traditional stratification”) to failure rates when stratifying for predicted survival using a predictive algorithm. We simulated virtual trials using the PRO ‐ ACT database without application of a treatment effect to assess balance between cohorts. We performed 100 randomizations using each stratification method – traditional and algorithmic. We applied these stratification schemes to a randomization simulation with a treatment effect using survival as the endpoint and evaluated sample size and power. Results Stratification by predicted survival met with fewer failures than traditional stratification. Stratifying predicted survival into tertiles performed best. Stratification by predicted survival was validated with an external dataset, the placebo arm from the BENEFIT ‐ ALS trial. Importantly, we demonstrated a substantial decrease in sample size required to reach statistical power. Conclusions Stratifying randomization based on predicted survival using a machine learning algorithm is more likely to maintain balance between trial arms than traditional stratification methods. The methodology described here can translate to smaller, more efficient clinical trials for numerous neurological diseases.