Synergy Area with FDR-controlled Evaluation (SAFE) to robustly assess safety profile in clinical trials

TL;DR

The paper introduces SAFE, a two-layer framework that robustly assesses drug safety in clinical trials by evaluating clinically meaningful areas and controlling false discovery rates, ensuring reliable safety conclusions.

Contribution

It proposes a novel SAFE framework that emphasizes substantial evidence and error rate control for safety assessment, improving robustness over existing methods.

Findings

SAFE controls error rates within and across areas at the nominal level.

SAFE effectively screens out extreme data and reaches solid safety conclusions.

Application to real data demonstrates SAFE's practical utility and robustness.

Abstract

Safety assessment plays a fundamental role in developing a new drug via clinical trials for ethical considerations. Due to complexity, manual review is typically conducted on the totality of data to draw safety conclusions. There are some existing quantitative methods to facilitate or tailor further medical review, with a controlled error rate and integration of clinical knowledge. In addition to those two key aspects, we emphasize the importance of relying on substantial evidence to draw robust conclusions on safety. Motivated by these three important properties, we propose a two-layer Synergy Area with FDR-controlled Evaluation (SAFE) structural framework to robustly assess the safety profile in clinical trials. In the first layer of SAFE, we investigate each clinically meaningful Synergy Area (SA) based on compelling evidence. In the next layer, the false discovery rate (FDR) is controlled for potential findings across all SAs. Simulation studies show that SAFE properly controls error rates within and across SAs at the nominal level. We further apply the proposed approach to two case studies based on real data from the Historical Trial Data (HTD) Sharing Initiative of the DataCelerate platform. As compared to some direct methods, SAFE demonstrates an appealing feature of screening out extreme data and reaching solid safety conclusions. It can act as either a building block in another framework, or a platform to incorporate additional components.