Advancing translational science through trial integrity: REDCap-based approaches to mitigating fraud and bias

TL;DR

This paper introduces two tools for improving data integrity in decentralized clinical trials by reducing fraud and sampling bias.

Contribution

The paper presents two new REDCap modules, Cheatblocker and QuotaConfig, designed to mitigate fraud and sampling bias in DCTs.

Findings

Cheatblocker and QuotaConfig were successfully implemented to enhance data integrity in two case examples.

These tools offer a practical solution to common threats in decentralized clinical trials.

The use of such tools can improve research rigor and public health outcomes in translational science.

Abstract

Decentralized clinical trials (DCTs) have the potential to increase pace and reach of recruitment as well as to improve sample representation, compared to traditional in-person clinical trials. However, concerns linger regarding data integrity in DCTs due to threats of fraud and sampling bias. The purpose of this report is to describe two tools that we have developed and successfully implemented to combat these threats. Cheatblocker and QuotaConfig are two external modules that we have made publicly available within the REDCap data capture system to target fraud and sampling bias, respectively. We describe the modules, present two case examples in which we used the modules successfully, and discuss the potential impact of tools such as these on data integrity in DCTs. We situate this discussion within the broader landscape of translational science wherein we strive to improve research rigor and efficiency to maximize public health benefit.

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