Amos Okutse’s master's thesis entitled “Machine Learning (ML) Methods for Bias Correction and Precision Optimization Using Covariate Adjustment in Randomized Trials with Missing Data” examined the promise of machine learning algorithms to yield precise and unbiased treatment effect estimates in randomized trials characterized by missing outcome data relative to standard analysis algorithms such as multiple linear regression under the additional complication of misspecification of the adjustment model. His research sought to provide additional evidence-based guidance for the appropriate use of ML adjustment in randomized trials for optimal efficiency gains in treatment effects estimation even under uncertainty about the true outcome data generating mechanism when outcomes are observed or are missing. For this work, Amos received the Dean’s 2023 Master of Science Graduate Student Poster Award at Brown University’s annual public health research day poster competition. He was also the recipient of the Brown University Department of Biostatistics Master of Science Thesis Award of Excellence. Amos is continuing at Brown in the PhD in Biostatistics under continued NAMBARI support.
Rophence Ojiambo graduated with a Master of Science in Biostatistics. Under the mentorship of her advisors, she completed a thesis titled "Generalizing Cluster Randomized Control Trial Results to a Target Population." Her research on population-based inference delves into the crucial task of extending the findings of clinical trials to wider populations, offering valuable insights for future studies and policy implementations. Rophence has returned to Kenya as a trainee with the D43 grant.