Data Handling and Reporting for Ongoing Clinical Trials During COVID-19

he COVID-19 outbreak has significantly impacted the clinical research industry. Analysis conducted on data obtained from www.clinicaltrials.gov on March 31, 2020, identified more than 9,250 industry-funded clinical trials that could be impacted. Regulatory agencies in the EU, US, and elsewhere have published guidance on how clinical trial sponsors could approach these ongoing studies. This article will specifically focus on how to handle data-related challenges from the statistical point of view, taking regulators’ guidance into account.

Ongoing clinical trials face multiple risks due to COVID-19, including:

• Inability of the patient to visit the clinical site
• Lack of adherence to protocol-specific procedures due to movement restrictions
• Travel restrictions impacting resupply of investigational medicinal products to sites and patients
• Additions to hospitals’ existing workload burden to treat the pandemic, which can further delay and reprioritize clinical protocol-related activities.

This article considers how these overarching challenges impact the analysis of studies and how risk may be mitigated when reporting to regulatory authorities in these three key areas: (1) adverse event data reporting, (2) handling missing data, and (3) clinical study reports (CSRs).

Adverse Event Data Reporting

All COVID-19 related regulatory guidances state that a sponsor’s highest priority should be the potential impact on the safety of trial participants. The investigators’ ability to monitor safety is a primary task in all trials. With site visits being disrupted, this process has become complex and monitoring key outcomes has become potentially more difficult.

Investigators responsible for monitoring the safety of patients must evaluate whether alternative methods such as telephone contact, virtual visits, or alternative locations for safety assessments could be implemented as advised by regulatory guidance. Even so, the adverse event (AE) and serious adverse event (SAE) reporting guidelines within the protocol must be reviewed and followed, and, as much as possible, patients from all periods, whether they are in baseline, follow-up or discontinuation, must be able to report their AEs and SAEs.

In addition to difficulties with the completeness of AE data, the need to identify adverse events related to COVID-19 is hampered by the fact that this is a new disease for which previous versions of MedDRA did not have specific codes, introducing additional complication to statistical analysis. MedDRA maintenance and support services have released an emergency version of MedDRA using the same version number (23.0) which includes COVID-19 terms. Modification of AE forms in ongoing trials is quite difficult. One alternative is for investigative sites to use keyword text “COVID-19” in the verbatim event description. The below sample data includes existing standard variables as well as non-standard variables to indicate the relationship of the adverse event to COVID-19.

Handling Missing Data

In cases where protocol-specified data have not or cannot be collected, it is important to capture specific information in the case report form (CRF) that explains the basis of the missing data, including the impact of COVID-19 on the missing data. Data may be missing due to missing visits, missing tests, missing assessments, missing investigational medicinal product (IMP) administrations, and/or missing scheduled procedures.

From a statistical data analysis perspective, a recommended approach will be to conduct subgroup analysis by pre-COVID, during COVID and post-COVID phase of the trial to understand the impact of COVID-19 on missing data in the trial. These can be documented in the protocol, and the statistical analysis plan (SAP) may be amended to include these distinctions.

This significantly impacts existing SAP sections on handling drop-outs, imputation methods, and subgroup analysis review. Some protocols may include already prescribed techniques for imputation of partial and missing dates. The same could be followed for all emerging missing data as well. All missing safety-related information must consider assumption of the worst-case scenario unless proven otherwise (for example, the default should be treatment-emergent AEs and the AE onset date to be compared with first dose of medication or baseline).

Although the protocol deviation domain in the study data tabulation model (SDTM) could be used to represent missed visits, the custom events domain could also be used to document all visits. This approach accommodates amending protocols to reflect altered visit schedules or all remote visits, and it also permits information about visits before and after any protocol amendments to be represented in a single domain.

In these sample data, we can include non-standard variables apart from existing standard variables to indicate the missing visit-related information due to COVID-19.

Non-standard variables can be considered to flag these records:

• VEREASOC to represent reason for missed visits
• VEEPCHGI to represent pandemic-related change indicator
• VECNTMOD to represent a method for conducting a visit that differed from the method originally planned in the protocol.

In the above sample data, the first row shows visits that occurred as planned, the second row shows visits missed for reasons related to the COVID-19 pandemic, the second to last row shows visits conducted remotely due to COVID-19, and the last row shows visits that deviated from the original plan because some assessments were not done due to COVID-19.

CSR Reporting

To adhere to regulatory guidance for COVID-19, multiple types of information must be included in the CSRs for all ongoing trials.

This includes listing contingency measures that were implemented during study disruption, which could be derived from protocol amendments, an appendix to monitoring guidelines, and data or document management plans and documentation in the trial master file (TMF). The report must also address the impact of implemented contingency measures on the reported safety and efficacy results, a list of all participants affected by COVID-related study disruptions, and specifics about how each participant was disrupted. This includes such study domains as protocol deviations, disposition, product accountability, procedures, AE, exposure, site transfer, and so on.

Finally, specific reasons for COVID-related protocol deviations must be provided in detail. For example, if a participant missed any scheduled visits, the reason must be provided—such as lack of transportation due to the pandemic—instead of simply listing “visit missed.”

Summary

COVID-19 will not be temporary. Until mass vaccination can be established, it will become a global way of life for a few more years. This means that clinical trials will continue to face challenges like missing data, protocol deviations, and travel restrictions for patients and investigators. It is imperative that viable means of handling these data challenges be implemented without compromise, so that patient safety and trial data integrity are maintained.