Work

SECRETS: Subject-efficient clinical randomized controlled trials using synthetic intervention

Year: 2024

Type: article

Abstract: Background: The parallel-group randomized controlled trial (RCT) is commonly used in Phase-3 clinical trials to establish treatment effectiveness but requires hundreds-to-thousands of subjects, making it difficult to implement, which leads to high Phase-3 trial failure rates. One approach to increasing power of a trial is to augment data collected from an RCT with external data from prospective studies or prior RCTs. However, this requires that external data be comparable to data from the study of interest, a condition that does not hold for new interventions or populations being studied. Another approach is to lower sample size requirements by using the cross-over design, which measures individual treatment effects (ITEs) to remove inter-subject variability; however, this design is only suitable for chronic conditions and interventions with effects that wash out rapidly. Method: We propose a novel and practical framework called SECRETS (Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention) to increase power of any parallel-group RCT by simulating the cross-over design using only data collected from the study. SECRETS first estimates ITEs across all subjects recruited to the RCT by using a state-of-the-art counterfactual estimation algorithm called synthetic intervention (SI). Since SI induces dependencies among the ITEs, we introduce a novel hypothesis testing strategy to test for treatment effectiveness. Results: % (10-3,957 fewer subjects per arm). Our analyses show that SECRETS increases power by consistently reducing the variance of the average treatment effect, thereby mimicking the effects of a cross-over design. Conclusion: SECRETS increases subject efficiency of an RCT by simulating the cross-over design using only data collected from the RCT; therefore, it is a feasible solution for increasing the trial's power, especially under settings where satisfying sample size requirements is difficult.

Source: Contemporary Clinical Trials Communications

DOI: 10.1016/j.conctc.2024.101265

Authors: Sayeri Lala, Niraj K. Jha

Institution: Princeton University

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,350