Clinical Data Management CDM is a critical phase in clinical research, which 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.
Clinical trial data management pdf
Team members of CDM are actively involved in all stages of clinical trial right from inception to completion.
They should have adequate process knowledge that helps maintain the quality standards of CDM processes. Various procedures in CDM including 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. In the present scenario, there is an increased demand to improve the CDM standards to meet the regulatory requirements and stay ahead of the competition by means of faster commercialization of product.
With the implementation of regulatory compliant data management tools, CDM team can meet these demands. Additionally, it is becoming mandatory for companies to submit the data electronically.
CDM professionals should meet appropriate expectations and set standards for data quality and also have a drive to adapt to the rapidly changing technology. This article highlights the processes involved and provides the reader an overview of the tools and standards adopted as well as the roles and responsibilities in CDM.
Clinical trial is intended to find answers to the research question by means of generating data for proving or disproving a hypothesis. The quality of data generated plays an important role in the outcome of the study. All researchers try their hands on CDM activities during their research work, knowingly or unknowingly.
Clinical data management
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.
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[ 2 ] 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. The data should also meet the applicable regulatory requirements specified for data quality. 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. 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.
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. In regulatory submission studies, maintaining an audit trail of data management activities is of paramount importance. These CDM tools ensure the audit trail and help in the management of discrepancies.
According to the roles and responsibilities explained later , multiple user IDs can be created with access limitation to data entry, medical coding, database designing, or quality check. This ensures that each user can access only the respective functionalities allotted to that user ID and cannot make any other change in the database.
For responsibilities where changes are permitted to be made in the data, the software will record the change made, the user ID that made the change and the time and date of change, for audit purposes audit trail. During a regulatory audit, the auditors can verify the discrepancy management process; the changes made and can confirm that no unauthorized or false changes were made. Akin to other areas in clinical research, CDM has guidelines and standards that must be followed.
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. 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.
If data have to be submitted to regulatory authorities, it should be entered and processed in 21 CFR part compliant systems.
Most of the CDM systems available are like this and pharmaceutical companies as well as contract research organizations ensure this compliance. 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. The SDTMIG standard[ 4 ] describes the details of model and standard terminologies for the data and serves as a guide to the organization. CDASH v 1.
The CDM process, like a clinical trial, begins with the end in mind. This means that the whole process is designed keeping the deliverable in view. As a clinical trial is designed to answer the research question, the CDM process is designed to deliver an error-free, valid, and statistically sound database.
To meet this objective, the CDM process starts early, even before the finalization of the study protocol. The protocol is reviewed from a database designing perspective, for clarity and consistency.
During this review, the CDM personnel will identify the data items to be collected and the frequency of collection with respect to the visit schedule. The data fields should be clearly defined and be consistent throughout.
The type of data to be entered should be evident from the CRF. For example, if weight has to be captured in two decimal places, the data entry field should have two data boxes placed after the decimal as shown in Figure 1.
Similarly, the units in which measurements have to be made should also be mentioned next to the data field.
Clinical Data Management (CDM )Training for Beginners
Along with the CRF, the filling instructions called CRF Completion Guidelines should also be provided to study investigators for error-free data acquisition.
Annotations are coded terms used in CDM tools to indicate the variables in the study.
An example of an annotated CRF is provided in Figure 1. In questions with discrete value options like the variable gender having values male and female as responses , all possible options will be coded appropriately. Annotations are entered in coloured text in this figure to differentiate from the CRF questions.
DMP document is a road map to handle the data under foreseeable circumstances and describes the CDM activities to be followed in the trial.
A list of CDM activities is provided in Table 1. The edit check programs in the DVP help in cleaning up the data by identifying the discrepancies. Databases are the clinical software applications, which are built to facilitate the CDM tasks to carry out multiple studies.
Study details like objectives, intervals, visits, investigators, sites, and patients are defined in the database and CRF layouts are designed for data entry.
These entry screens are tested with dummy data before moving them to the real data capture. Data collection is done using the CRF that may exist in the form of a paper or an electronic version. The traditional method is to employ paper CRFs to collect the data responses, which are translated to the database by means of data entry done in-house.
These paper CRFs are filled up by the investigator according to the completion guidelines. In e-CRF method, chances of errors are less, and the resolution of discrepancies happens faster.
Since pharmaceutical companies try to reduce the time taken for drug development processes by enhancing the speed of processes involved, many pharmaceutical companies are opting for e-CRF options also called remote data entry.
CRFs are tracked for missing pages and illegible data manually to assure that the data are not lost.
In case of missing or illegible data, a clarification is obtained from the investigator and the issue is resolved. Data entry takes place according to the guidelines prepared along with the DMP. This is applicable only in the case of paper CRF retrieved from the sites.
Usually, double data entry is performed wherein the data is entered by two operators separately. Moreover, double data entry helps in getting a cleaner database compared to a single data entry. Earlier studies have shown that double data entry ensures better consistency with paper CRF as denoted by a lesser error rate.
Data validation is the process of testing the validity of data in accordance with the protocol specifications.
Data management in clinical research: An overview
Edit check programs are written to identify the discrepancies in the entered data, which are embedded in the database, to ensure data validity. These programs are written according to the logic condition mentioned in the DVP. These edit check programs are initially tested with dummy data containing discrepancies.
Discrepancy is defined as a data point that fails to pass a validation check. Discrepancy may be due to inconsistent data, missing data, range checks, and deviations from the protocol.
In e-CRF based studies, data validation process will be run frequently for identifying discrepancies. These discrepancies will be resolved by investigators after logging into the system.
Ongoing quality control of data processing is undertaken at regular intervals during the course of CDM. For example, if the inclusion criteria specify that the age of the patient should be between 18 and 65 years both inclusive , an edit program will be written for two conditions viz. If for any patient, the condition becomes TRUE, a discrepancy will be generated. DCFs are documents containing queries pertaining to the discrepancies identified.
This is also called query resolution.
Discrepancy management includes reviewing discrepancies, investigating the reason, and resolving them with documentary proof or declaring them as irresolvable. Discrepancy management helps in cleaning the data and gathers enough evidence for the deviations observed in data. Almost all CDMS have a discrepancy database where all discrepancies will be recorded and stored with audit trail.