Danielle Beaulieu, Jonavelle Cuerdo, Mark Schactman, David L. Ennist, Albert Taylor

The poster will be presented at the MDA Clinical & Scientific Conference on March 13-16 2022.

Background: Adding covariates to linear regression models is a well-known method for reducing variability
and improving the estimates of statistical analyses. In clinical trials, covariate adjustment can
yield a more precise measure of the treatment effect. Typically, adjustment is done using a few
baseline prognostic factors thought to be important in disease progression. From the Food and
Drug Administration (FDA) draft guidance on adjusting for covariates: “incorporating
prognostic baseline factors in the primary statistical analysis of clinical trial data can result in a
more efficient use of data to demonstrate and quantify the effects of treatment with minimal
impact on bias or the Type I error rate”. We developed machine-learning models to predict
standard Amyotrophic Lateral Sclerosis (ALS) outcomes – the Revised ALS Functional Rating
Scale, percent expected vital capacity (PEXP-VC), and survival – using a constellation of
baseline factors. We assessed the utility of these predictions as covariates over baseline
prognostic factors that are typically used in ALS clinical trials.

Conclusions: Key conclusions from this work are:

  • The optimal covariates are the predictions matched to the outcome of interest (i.e., predicted
    ALSFRS-R is the optimal covariate for the ALSFRS-R outcome, predicted vital capacity is the
    optimal covariate for the vital capacity outcome, etc.)
  • Riluzole was a very weak covariate and time since symptom onset was a strong covariate
  • A single ML prediction achieved better results than a combination of five baseline values that
    include ALSFRS-R, percent expected vital capacity, age, time from symptom onset to baseline,
    and riluzole use.

Generating predictions for other disease areas could produce similar benefits and speaks to the
need for aggregated historical clinical trial data to improve the planned analyses of clinical trials

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