coClinical data management leads to generation of high-quality, reliable, and statistically sound data from clinical trials. This helps to produce a drastic reduction in time from drug development to marketing. It ensures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to ensure that conclusions drawn from research are well supported by the data. Achieving this goal protects public health and increases confidence in marketed therapeutics.
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Clinical data management (CDM) is a complex web of processes. These include, Case Report Form (CRF) designing, CRF annotation, database designing, data-entry, data validation, discrepancy management, medical coding, data extraction, and database locking are assessed for quality at regular intervals during a trial. However, after knowing about the complex nature of CDM, the first question that hits are mind is, what exactly is Clinical data management? And what is the significance of CDM?
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What is Clinical Data Management (CDM)?
Clinical data management is the important part of the clinical trials process. Almost every researcher utilizes this process knowingly or unknowingly while carrying out their research work. CDM basically 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.
Significance of CDM
All the Health products directly indulge with the patient’s health so it very important to have all the knowledge about the health product before it being used. Here, CDM comes into play, 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.
How Does Clinical Data Management (CDM) Work?
There are few pre-requisites for CDM and those are:
Standard operating procedures:
Standard operating procedures (SOPs) describe the process to be followed in conducting data management activities and support the obligation to follow applicable laws and guidelines (e.g., ICH GCP and 21CFR Part 11) in the conduct of data management activities.
Data management plan:
The data management plan describes the activities to be conducted in the course of processing data. Key topics to cover include the SOPs to be followed, the clinical data management system (CDMS) to be used, description of data sources, data handling processes, data transfer formats and process, and quality control procedure.
Case report form design:
The case report form (CRF) is the data collection tool for the clinical trial and can be paper or electronic. Paper CRFs will be printed, often using No Carbon Required paper, and shipped to the investigative sites conducting the clinical trial for completion after which they are couriered back to Data Management. Electronic CRFs enable data to be typed directly into fields using a computer and transmitted electronically to Data Management.
Database design and build:
For a clinical trial utilizing an electronic CRF database design and CRF design are closely linked. The electronic CRF enables entry of data into an underlying relational database. For a clinical trial utilizing a paper CRF, the relational database is built separately. In both cases, the relational database allows entry of all data captured on the CRF.
Computerized system validation (CSV):
All computer systems used in the processing and management of clinical trial data must undergo validation testing to ensure that they perform as intended and that results are reproducible.
Clinical Data Interchange Standards Consortium (CDISC):
The Clinical Data Interchange Standards Consortium leads the development of global, system independent data standards which are now commonly used as the underlying data structures for clinical trial data. These describe parameters such as the name, length and format of each data field (variable) in the relational database.
Validation rules:
Validation Rules are electronic checks defined in advance which ensure the completeness and consistency of the clinical trial data.
User acceptance testing:
Once an electronic CRF (eCRF) is built, the clinical data manager (and other parties as appropriate) conducts User Acceptance Testing (UAT). The tester enters test data into the e-
CRF and record whether it functions as intended. UAT is performed until all the issues (if found) are resolved.
Once these pre- requisites are fulfilled, we enter the active phase which include following processes:
Data entry:
When an electronic CRF is in use data entry is carried out at the investigative site where the clinical trial is conducted by site staff who have been granted appropriate access to do so.
When using a paper CRF the pages are entered by data entry operators. Best practice is for a first pass data entry to be completed followed by a second pass or verification step by an independent operator. Any discrepancies between the first and second pass may be resolved such that the data entered is a true reflection of that recorded on the CRF. Where the operator is unable to read the entry the clinical data manager should be notified so that the entry may be clarified with the person who completed the CRF.
Data validation:
Data validation is the application of validation rules to the data. For electronic CRFs the validation rules may be applied in real time at the point of entry. Offline validation may still be required (e.g., for cross checks between data types)
Data queries:
Where data entered does not pass validation rules then a data query may be issued to the investigative site where the clinical trial is conducted to request clarification of the entry. Data queries must not be leading (i.e., they must not suggest the correction that should be made). For electronic CRFs only the site staff with appropriate access may modify data entries. For paper CRFs, the clinical data manager applies the data query response to the database and a copy of the data query is retained at the investigative site. When an item or variable has an error or a query raised against it, it is said to have a “discrepancy” or “query”.
All electronic data capture (EDC) systems have a discrepancy management tool or also refer to “edit check” or “validation check” that is programmed using any known programming language (e.g., SAS, PL/SQL, SQL, Python, etc).
Central laboratory data:
Samples collected during a clinical trial may be sent to a single central laboratory for analysis. The clinical data manager liaises with the central laboratory and agrees data formats and transfer schedules in Data Transfer Agreement. The sample collection date and time may be reconciled against the CRF to ensure that all samples collected have been analyzed.
Other external data:
Analysis of clinical trial data may be carried out by laboratories, image processing specialists or other third parties. The clinical data manager liaises with such data providers and agree data formats and transfer schedules. Data may be reconciled against the CRF to ensure consistency.
Serious adverse event reconciliation:
The CRF collects adverse events reported during the conduct of the clinical trial however there is a separate process which ensures that serious adverse events are reported quickly. The clinical data manager must ensure that data is reconciled between these processes.
Patient recorded data:
Where the subject is required to record data (e.g., daily symptoms) then a diary is provided for completion. Data management of this data requires a different approach to CRF data as, for example, it is generally not practical to raise data queries. Patient diaries may be developed in either paper or electronic (eDiary) formats. Such eDiaries generally take the form of a handheld device which enables the subject to enter the required data and transmits this data to a centralised server.
Database finalization and extraction:
Once all expected data is accounted for, all data queries closed, all external data received and reconciled and all other data management activities complete the database may be finalized.
Typical reports generated and used by the clinical data manager includes:
1. Status of page completion / missing pages
2. Status of data queries
3. Data queries not resolved within specified time limit
4. Commonly raised data queries (to help identify areas where improvements can be made)
Quality Control is applied at various stages in the Clinical data management process and is normally mandated by SOP.
Regulations, guidelines and in CDM
Since the pharmaceutical industry relies on the electronically captured data for the evaluation of medicines, there is a need to follow good practices in CDM and maintain standards in electronic data capture. These electronic records have to comply with a Code of Federal Regulations (CFR), 21 CFR Part 11. This regulation is applicable to records in electronic format that are created, modified, maintained, archived, retrieved, or transmitted. This demands the use of validated systems to ensure accuracy, reliability, and consistency of data with the use of secure, computer-generated, time-stamped audit trails to independently record the date and time of operator entries and actions that create, modify, or delete electronic records. Adequate procedures and controls should be put in place to ensure the integrity, authenticity, and confidentiality of data. If data have to be submitted to regulatory authorities, it should be entered and processed in 21 CFR part 11-compliant systems. Most of the CDM systems available are like this and pharmaceutical companies as well as contract research organizations ensure this compliance.
Society for Clinical Data Management (SCDM) publishes the Good Clinical Data Management Practices (GCDMP) guidelines, a document providing the standards of good practice within CDM. GCDMP was initially published in September 2000 and has undergone several revisions thereafter. The July 2009 version is the currently followed GCDMP document. GCDMP provides guidance on the accepted practices in CDM that are consistent with regulatory practices. Addressed in 20 chapters, it covers the CDM process by highlighting the minimum standards and best practices.
Clinical Data Interchange Standards Consortium (CDISC), a multidisciplinary non-profit organization, has developed standards to support acquisition, exchange, submission, and archival of clinical research data and metadata. Metadata is the data of the data entered. This includes data about the individual who made the entry or a change in the clinical data, the date and time of entry/change and details of the changes that have been made. Among the standards, two important ones are the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) and the Clinical Data Acquisition Standards Harmonization (CDASH) standards, available free of cost from the CDISC website (www.cdisc.org). The SDTMIG standard describes the details of model and standard terminologies for the data and serves as a guide to the organization. CDASH v 1.1 defines the basic standards for the collection of data in a clinical trial and enlists the basic data information needed from a clinical, regulatory, and scientific perspective.
CDM has evolved in response to the ever-increasing demand from pharmaceutical companies to fast-track the drug development process and from the regulatory authorities to put the quality systems in place to ensure generation of high-quality data for accurate drug evaluation. To meet the expectations, there is a gradual shift from the paper-based to the electronic systems of data management. Developments on the technological front have positively impacted the CDM process and systems, thereby leading to encouraging results on speed and quality of data being generated.
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