Clinical Data Management & Biometrics, Software, Clinical Databases, Database Programming, e-Clinical Technology Solutions, Electronic Data Capture (EDC), Clinical Development Document Management, Electronic Laboratory Notebook System, Rapid Data Delivery, Electronic Investigative Site Files (eISF), Electronic Data Transfer (EDT), Medical Coding (MedDRA, WHODrug), Database Design, eCRF Design & System Configuration, Phase I EDC Solution, data quality
Source data are generated. Common examples of source data are clinical site medical records, laboratory results, and patient diaries. If paper Case Report Forms (CRFs) are being used, the clinical site records are transcribed onto the CRFs. Data from the CRFs, as well as other source data, are entered into the clinical trial database. Electronic CRFs (eCRFs) allow data to be entered directly into the database from source documents. Data from paper CRFs are often entered twice and and reconciled in order to reduce the error rate. The data are checked for accuracy, quality, and completeness, and problems are resolved. This often involves queries to the clinical site.
The database is locked when the data are considered final. The data are reformatted for reporting and analysis. Tables, listings, and figures are generated. The data are analyzed, and the analysis results are reported. When significant results are found, this step may result in the generation of additional tables, listings, or figures. The results are integrated into high-level documentation such as Investigator’s Brochures (IBs) and Clinical Study Reports (CSRs). The database and other study data are archived. The source of the data is known and attributable. The data are legible: readable and comprehensible to humans. The data are recorded when they are generated i.e. should be contemporaneous, the original data are the first recording from the primary source. Accurate data are correct.Practically, quality data also requires at least three other aspects i.e. data are readily available, transmissible, and storable; data are complete and unbiased; data are in a format that is internally consistent and compliant with or readily transformable to accepted standards.
21 CFR 11 details the predicate rules that are required to insure that electronic records are “trustworthy [and] reliable”. Proper implementation of 21 CFR 11 helps ensure that the Attributable, Legible, Original, and Accurate aspects of the ALCOA standard are met.
Validation of clinical data management programs and procedures is required to document that clinical data management standards are met.
Clinical trial data management service providers provide end to end comprehensive clinical data management solutions from Phase I through to post-marketing trials. Team of global clinical data management experts are committed to upholding a standardized, process-driven approach.
Various clinical data management services comprise on-shore and off-shore Clinical Data Management Services, EDC and Paper Trial Set-Up & Management, Certified Medidata RAVE Builders, Access to other EDC solutions (Low Cost, Oracle etc.), Data Management Plan (DMP) development, Data Management Project Management, CRF/eCRF design and development, CRF Annotation & Review, Database Build & Design, Data validation specifications, Edit checks Programming & Testing, Data processing through double data entry, Query Management, Medical Coding, Safety Data Management & Reconciliation, Data Export/Transfer, CDASH Compliant deliverables, Real-time data viewing and reporting.
Clinical data management is a relevant and important part of a clinical trial. All researchers try their hands on CDM activities during their research work, knowingly or unknowingly. Without identifying the technical phases, we undertake some of the processes involved in CDM during our research work. This article highlights the processes involved in CDM and gives the reader an overview of how data is managed in clinical trials.
CDM is the process of collection, cleaning, and management of subject data in compliance with regulatory standards. The primary objective of CDM processes is to provide high-quality data by keeping the number of errors and missing data as low as possible and gather maximum data for analysis. To meet this objective, best practices are adopted to ensure that data are complete, reliable, and processed correctly. This has been facilitated by the use of software applications that maintain an audit trail and provide easy identification and resolution of data discrepancies. Sophisticated innovations have enabled CDM to handle large trials and ensure the data quality even in complex trials.
High-quality data should be absolutely accurate and suitable for statistical analysis. These should meet the protocol-specified parameters and comply with the protocol requirements. This implies that in case of a deviation, not meeting the protocol-specifications, we may think of excluding the patient from the final database. It should be borne in mind that in some situations, regulatory authorities may be interested in looking at such data. Similarly, missing data is also a matter of concern for clinical researchers. High-quality data should have minimal or no misses. But most importantly, high-quality data should possess only an arbitrarily ‘acceptable level of variation’ that would not affect the conclusion of the study on statistical analysis. The data should also meet the applicable regulatory requirements specified for data quality.
Many software tools are available for data management, and these are called Clinical Data Management Systems (CDMS). In multicentric trials, a CDMS has become essential to handle the huge amount of data. Most of the CDMS used in pharmaceutical companies are commercial, but a few open source tools are available as well. Commonly used CDM tools are ORACLE CLINICAL, CLINTRIAL, MACRO, RAVE, and eClinical Suite. In terms of functionality, these software tools are more or less similar and there is no significant advantage of one system over the other. These software tools are expensive and need sophisticated Information Technology infrastructure to function. Additionally, some multinational pharmaceutical giants use custom-made CDMS tools to suit their operational needs and procedures. Among the open source tools, the most prominent ones are OpenClinica, openCDMS, TrialDB, and PhOSCo. These CDM software are available free of cost and are as good as their commercial counterparts in terms of functionality. These open source software can be downloaded from their respective websites.