  Clinical trials are critical in advancing medical treatments but often suffer
from immense time and financial burden. Advances in statistical methodologies
and artificial intelligence (AI) present opportunities to address these
inefficiencies. Here we introduce Prognostic Covariate-Adjusted Mixed Models
for Repeated Measures (PROCOVA-MMRM) as an advantageous combination of
prognostic covariate adjustment (PROCOVA) and Mixed Models for Repeated
Measures (MMRM). PROCOVA-MMRM utilizes time-matched prognostic scores generated
from AI models to enhance the precision of treatment effect estimators for
longitudinal continuous outcomes, enabling reductions in sample size and
enrollment times. We first provide a description of the background and
implementation of PROCOVA-MMRM, followed by two case study reanalyses where we
compare the performance of PROCOVA-MMRM versus the unadjusted MMRM. These
reanalyses demonstrate significant improvements in statistical power and
precision in clinical indications with unmet medical need, specifically
Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS). We also
explore the potential for sample size reduction with the prospective
implementation of PROCOVA-MMRM, finding that the same or better results could
have been achieved with fewer participants in these historical trials if the
enhanced precision provided by PROCOVA-MMRM had been prospectively leveraged.
We also confirm the robustness of the statistical properties of PROCOVA-MMRM in
a variety of realistic simulation scenarios. Altogether, PROCOVA-MMRM
represents a rigorous method of incorporating advances in the prediction of
time-matched prognostic scores generated by AI into longitudinal analysis,
potentially reducing both the cost and time required to bring new treatments to
patients while adhering to regulatory standards.
