The success of every clinical trial relies on data accurately generated, gathered, and analyzed. Due to its high numbers involved, drug manufacturers, manufacturers for medical devices, biologics, as well as other life sciences organizations are turning to Importance of Clinical Data Management (CDM) alternatives to assure their quality of the data. At the very same time, CDM alternatives also assist organizations throughout the life sciences secure or protect patient information and data from participants in clinical trials.
Clinical data management (CDM) process in its entirety – from protocol review and CRF design through database lock. Describing the roles of various CDM team members and tips for efficient data management practices, “The Clinical Data Management Process” provides a comprehensive yet concise summary of this essential function in clinical trial research, specifically with respect to retina trials. “The Clinical Data Management Process”
Importance of Clinical Data Management
Drug developers want to ensure that the data delivered to regulatory bodies is reliable; from an ethical perspective, clinical data inform treatment decisions and ultimately affect patient health. For both of these reasons, clinical data quality and integrity are crucial. Although it seems obvious that data management only happens after the data are collected, the process actually starts before the study protocol is finalized.
It is among a pharmaceutical company’s most valuable and important assets. It gives vital notification of the effectiveness and safety for drugs. Enormous amounts of data are gathered during the entire medical research life cycle.
The data analysis provides as the basis to:
A Case Report Form (CRF) is designed by the CDM team for data collection from protocol-specific activities. The CRF may exist in either a paper version or as electronic data capture (EDC). The CRF will be annotated with coded terms to communicate where the data collected for each question is to be stored in the database.
The next item developed is the Data Management Plan (DMP), which details how the data are to be handled according to how the study is anticipated to be run. A DMP describes the CDM activities to be followed in the trial, including trial master file maintenance, CRF specification, database design, data collection, CRF tracking, data entry, data storage and privacy, medical coding, data reconciliation (eg, serious adverse events and central laboratory data), data review, discrepancy management, data extraction, and database lock. The DMP is intended to standardize procedures and ensure that all CDM personnel understand the plan.
Next, a Data Validation Manual (DVM) is developed. This document contains the edit check programs for discrepancy identification.2 The edit check programs in the DVM help the site, monitoring, and CDM staff identify any discrepancies during data cleanup. Entry of data that are not validated may prompt a request for clarification. This process, called discrepancy management or query resolution, is put into place to investigate the reason for the discrepancies; ideally, discrepancies should be resolved quickly.2 Queries slow the progress of data analysis, so closing queries promptly helps to ensure that the trial continues to hit timelines without ignoring data points or losing subjects. EDC systems must capture any change to data after it has been saved and all discrepancies that are generated in an audit trail. Discrepancies should be reviewed at regular intervals by the CDM team to ensure that they are being resolved in a timely manner.
Coding of all medical terms reported allows standardization when it comes time for the data to be analyzed and reviewed.3 Information on adverse events, medical history, and concomitant medications must be coded in a uniform manner. This is especially important in multicenter trials in which multiple investigators collect and report information.
Finally, a database lock is put in place after all data management activities are complete to ensure there was no manipulation of study data after unmasking of the treatment groups and during the final analysis. A prelock checklist should be used to confirm that all necessary activities have been completed.
CDM process improvement follows a structural process and well-established system for preventing massive financial loss or valuable resources. Data authentication and catalogue process can undergo validation at each stage to avoid irreparable faults. It will make sure the consistent as well as useful importance of Clinical Data Management data.
Careful clinical data management is essential to the integrity of a clinical trial. Involving the CDM team early on ensures that a concrete data management plan is set forth from the start.