Approximately 10K diseases affect the population worldwide, yet only a small fraction of these diseases has some form of treatment, resulting in poor quality of life for tens to hundreds of millions of people across the globe. In response, biopharmaceutical companies have made substantial investments into research and development for drugs over the past decade, i.e., US$ 10B-100B per year; however, challenges with drug development greatly impede success, with only approximately 10% of developed drugs entering the market. While challenges emerge during all stages of drug development, which include discovery in the lab, pre-clinical testing, and clinical testing, addressing challenges with clinical testing, specifically, Phase-3 Randomized Controlled Trials (RCTs), remains paramount, given their critical role in obtaining market approval, combined with their high failure rates and high failure costs due to large sample size requirements. In this thesis, we make significant progress towards improving the efficiency of clinical RCTs by increasing their success rates and reducing their expenses with artificial intelligence using no or limited amounts of data collected before the RCT. 

First, we introduce SECRETS, a framework that reduces the sample size required for an RCT to demonstrate treatment effectiveness with high statistical accuracy. Specifically, SECRETS uses a state-of-the-art counterfactual estimation algorithm to simulate the cross-over trial, an RCT design that measures individual treatment effects to reduce intersubject variance and thereby improve sample efficiency. To further increase trial success rates, we introduce TAD-SIE, a framework for estimating the sample size required to establish the effectiveness of a treatment in the presence of poor sample size estimates stemming from insufficient prior data. TAD-SIE implements a novel trend-adaptive design tailored to SECRETS that adjusts the sample size estimate based on accrued RCT data while leveraging a sample-efficient hypothesis testing strategy. Finally, we present METRIK, a framework that reduces the total number of measurements that need to be collected per subject throughout the trial, thereby lowering trial expenses. To achieve this, METRIK learns a planned missing design and an associated imputation model using prior data collected from a small internal pilot study to identify correlations over time and across metrics.

zoom link: https://princeton.zoom.us/j/92256746685

Adviser: Niraj Jha