Central clinical monitoring plan

An integral part of running a Clinical Trial is to monitor and review the accumulated data in order to follow the progress of several important components like patients, sites, and the study endpoints. These in turn break down to measurable performance such as site compliance, patient outliers, treatment schedule in accordance with protocol, predefined endpoints, recruitment progression, and interim data cut-off reporting for the authorities. This monitoring process is described in the Central Clinical Monitoring Plan (CCMP), which outlines all needed outputs required for the study team to be able to successfully monitor the progress of the study.

Clinical and medical monitoring can take different forms. A popular approach has been the utilization of business intelligence to create accumulated dashboards that can visualize the data collected in the study. Another, used to expedite reporting to external review committees or the authorities, is to provide patient summaries and Case Report Summaries (CRS or Patient Profiles) for all enrolled patients.

For every study team the real challenge is to follow the development of the patients, sites, or study endpoints when you only have the raw data to scrutinize.

In this particular case, the customer presented a CCMP along with templates for the output they needed to have during the study. These consisted of several listings either summarizing patient progress, patient outliers in specific areas, or the collected data along with numerous KPI’s on study data totals. The customer also requested a CRS/patient Profile with more than 50 sections of data displayed containing around 1000 data points either displayed or calculated.

In Clinical Trials, the information is often spread out over different systems and providers so compiling available information into a solid foundation for decision making and monitoring requires technical expertise as well as deep knowledge of Clinical Trial governance and conduct.

The solution put forward was to consolidate all data points into an SDTM like format optimized to be robust and dynamic enough to handle all expected data. Using dynamic transformation data models, the information was then ready to be visualized, using a combination of Qlik tools. We then leveraged the flexibility of NPrinting to create the custom output, displaying all information as specified by the customer.

The customer was now able to request the output as frequently as they required, enabling them to create up to date reports with the same frequency as new data availability. The customer, in this case, created a bi-weekly schedule for the entire lifespan of the study which lasted around 2 years. This could be supplemented with ad hoc reports if required. The scheduled availability of the outputs made it far more efficient for the study team to monitor the progress of each patient, site, or endpoint, and played a huge role in facilitating reporting to the authorities with specific patient data presented in a readable format.