Blog Post

Data Integrity Considerations in Decentralized Trials

February 25, 2021

The COVID-19 pandemic has required the industry to adapt how clinical trials are run and realize the numerous benefits of decentralized clinical trials (DCTs). A wealth of information is available regarding the implementation of technologies that support DCTs. In addition, much has been written about the patient-level benefits DCTs can provide. Less discussed is how the collected data are managed once provided by the participant. These data need to be integrated with other data captured during the course of a clinical trial, and data integrity must be ensured in the absence of source data verification. Two topics will be discussed below to manage this integration and ensure confidence in the collected data.

Data for a DCT can be collected via several different avenues such as mobile technologies, mobile healthcare providers, electronic data capture (EDC) at the trial site, and third-party vendors such as central laboratories. It is critical to proactively outline how data will flow to a centralized location during the clinical trial. CTTI provides an example high-level data flow diagram that incorporates mobile technologies. This understanding is essential to ensure all data are accounted for and it is transparent to all members of the study team how data will get from point A to point B. In addition to a high-level mapping, the details of transfers must be documented also. This provides clarity regarding which individuals are responsible for sending and receiving the data and how data will be securely transferred and stored at each stage. Both CTTI and Transcelerate have published recommendations regarding implementation of decentralized trials and capture of non-EDC data that detail best practices for managing the flow of data.

Traditionally, sites entered clinical trial data in an EDC system and these data were source data verified to confirm accuracy. When data are collected directly from participants this is no longer an option. In this situation, additional steps need to be taken to ensure the quality and integrity of the data. Risk based monitoring has been widely adopted across the industry since the release of ICH E6(R2), but this monitoring often focuses on Key Risk Indicators (KRIs) that are site level metrics such as screen failure rates, query rates, SAEs reported, missed/late visits, etc. This type of monitoring is not sufficient for data collected directly from the participant or from non-CRF sources, and centralized statistical monitoring should be implemented to ensure the data are fit-for-purpose. Centralized statistical monitoring is based on all data across the clinical trial and not just pre-identified risks. This monitoring leads to early identification of anomalies, allowing the operations team to retrain sites or participants as needed and reduces the risk of regulatory submission failure due to inadequate data at the end of the study.

Centralized statistical monitoring allows us to review all collected data (both CRF and non-CRF) in a variety of methods. Some examples of routine checks include:

  • Examining data trends over time within and across sites
  • Examining data trends over time within and across participants
  • Identifying inconsistent data, data outliers, and skewed distributions
  • Evaluating for potential fraud based on lack of variability

A comprehensive understanding of all sources for data capture in a clinical trial and the process for centralization is key to conducting a decentralized clinical trial. In addition, it is pertinent to evaluate data collected centrally in real time. This centralized statistical monitoring allows early interventions to ensure data integrity for regulatory submission.


Heather Kopetskie, Director of Biostatistics, has over 16 years of experience in statistical planning, analysis, and reporting. She brings an extensive background of statistical and project leadership experience working on NIH and industry funded clinical trials in all phases of clinical development. Ms. Kopetskie has contributed to the publication of peer reviewed manuscripts and industry publications.