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. 2021 May:104:106368.
doi: 10.1016/j.cct.2021.106368. Epub 2021 Mar 26.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic

Most Alina Afroz  1 , Grant Schwarber  2 , Mohammad Alfrad Nobel Bhuiyan  3
Affiliations

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic

Most Alina Afroz et al. Contemp Clin Trials. 2021 May.

Abstract

Objectives: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk.

Methods: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality.

Conclusion: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

Keywords: Centralize data monitoring; Risk based data monitoring.

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Conflict of interest statement

The corresponding author states that, on behalf of all authors, there is no conflict of interest.

Figures

Fig. 2
Fig. 2
Ongoing Monitor study visits through a supervised monitoring dashboard. The “X” and blue circle indicate if the patient missed the dose for a specific visit or not. Here, “X” indicates if the patient missed a dose and a blue circle if they received their dose. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
(a) Box plot of Vital sign parameters by state, (b) Pearson correlation matrix plot for the vital signs.
Fig. 4
Fig. 4
Examples of first-digit distribution with Chi-square p-value. (a), The first digit followed a perfect uniform distribution with a p-value of 1.00 (b), where the first digit followed an abnormal distribution with p-value 0.0001, and (c), where the first digit followed the normal distribution with a p-value of 0.001.
Fig. 5
Fig. 5
Mahalanobis distance by study sites. (a) Large distance identified outliers, (b) Small distance determined inliers.
Fig. 1
Fig. 1
Flow chart for centralized data review process.
See this image and copyright information in PMC

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MeSH terms