A guide to creating a clinical trial data management plan
A data management plan (DMP) is essentially a roadmap guiding you through the process of collecting, storing, sharing and protecting data.
In this blog, we will work through the elements to include in a DMP and consider some tools that can be used for data capture.
What is a data management plan (DMP)?
A data management plan is a critical tool for ensuring the quality, integrity and responsible management of data in a clinical trial. A DMP guides the data lifecycle throughout the project.
It safeguards data quality, ensures regulatory compliance, and supports rigorous analysis and responsible data sharing.
A DMP should be developed as early as possible in the planning process and in conjunction with the protocol and human research ethics application (HREA).
What are the elements of a clinical trial DMP?
- Data quality and integrity: A DMP should clearly document how data will be managed throughout the trial. This should include details on how data will be collected, stored, and analysed. This helps to ensure that data collected during the trial is accurate, reliable, and of high quality. Clearly defining data collection methods and storage procedures facilitates an efficient data analysis process and is essential for maintaining the integrity of the trial and demonstrating transparency and accountability.
- Regulatory Compliance: Australia has regulatory requirements for the conduct of clinical trials, including guidelines from the Therapeutic Goods Administration (TGA) and the National Health and Medical Research Council (NHMRC). A DMP demonstrates that the research team has a plan to manage data responsibly and ethically in compliance with these guidelines, and reduces the risk of non-compliance penalties and delays in the approval process.
- Efficiency and risk management: Well-designed protocols and procedures in the DMP streamline data management processes and facilitate efficient trial conduct. By identifying potential risks and challenges related to data management upfront, a DMP allows researchers to implement strategies to mitigate these risks and minimise errors and delays.
- Participant safety and privacy: Clinical trials involving human participants often involve sensitive patient data, and individuals’ safety, rights, and privacy must be protected. A well-designed DMP includes protocols for handling sensitive participant data and identifies how data will be anonymised to ensure confidentiality and compliance with relevant privacy laws such as the Privacy Act 1988 (Cth) and the Australian Privacy Principles (APPs).
- Data sharing and accessibility: There is increasing emphasis on data sharing and accessibility in clinical research. National infrastructure developed through the Australian National Data Asset (HeSANDA) program enables researchers to access and share data from health studies, including clinical trials in Health Data Australia. A DMP can include provisions for data sharing, outlining how data will be curated, archived, and made available for future research purposes, in line with the FAIR principles (Findable, Accessible, Interoperable, and Reusable).
- Data storage: Storing data, whether electronically or in hard copy, involves specific considerations to ensure compliance with regulatory, ethical, and data security standards. The Privacy Act 1988, TGA Guidelines, and APPs identify specific requirements regarding the collection, storage, and sharing of personal and health information. Regardless of the data storage method used, standard operating procedures (SOPs) should be developed and maintained for both electronic and hard copy data storage. Regular audits of data storage practices should be conducted to ensure compliance with regulatory and internal policies. Participants should be informed about how their data will be stored and used in hard copy form, and consent must be obtained for such storage. Where possible, hard copy data should be de-identified to protect participant privacy.
- Data access and ownership: It is critical that personal and sensitive data is protected. Where possible, data should be de-identified to protect participant privacy. Secure methods (such as encryption and secure servers for electronic data and access-controlled environments for hard copy data) should be used for storing and transferring data. Data use agreements that clearly outline the terms of data access, usage, and sharing among all parties involved and data transfer agreements that outline the responsibilities and safeguards when transferring data between entities are important considerations. Role-Based Access Control (RBAC), which provides access to data based on the individual's role within the study, can ensure the protection of sensitive data by limiting access to only what is necessary for each individual’s specific duties. All individuals with access to the data should sign confidentiality agreements to ensure they understand and commit to protecting participant confidentiality. Detailed logs of who accesses the data, what data is accessed, and when should be kept to ensure accountability and traceability.
- Archiving and disposal: The National Statement on Ethical Conduct in Human Research and Good Clinical Practice (GCP) provides guidelines for data retention and archiving. Strict access controls should be put in place to ensure that only authorised personnel can access archived data. Standard Operating Procedures (SOPs) for data archiving, including detailed processes for labelling, storing, and accessing archived data, should be developed and maintained, as well as records of the archiving process, including the date of archiving, data description, and storage location. Typically, clinical trial data must be retained for at least 15 years after the completion of the trial, as required by the TGA. Approval should be obtained from HREC and regulatory bodies before disposing of data. Detailed records of data disposal activities, including what data was disposed of, the method of disposal, and the date of disposal should be kept.
Remember to consider statistics
There are several important aspects regarding statistics that should be considered in the design, conduct and analysis of a clinical trial in Australia. While consulting with statisticians or methodologists early in the trial planning process can help to address complex statistical issues effectively, researchers should consider the following statistical aspects to ensure validity, reliability and regulatory compliance of study findings.
- Sample size calculation: Determining an appropriate sample size is crucial for ensuring the statistical power of the trial – that is, the ability to detect meaningful differences or effects if they exist. Sample size calculations should be based on factors such as the desired level of statistical significance, expected effect size, variability of outcomes, and anticipated dropout rates.
- Randomisation and allocation concealment: Randomisation helps to ensure that participants are allocated to treatment groups in an unbiased manner, minimising the potential for selection bias. Allocation concealment further enhances the validity of randomisation by preventing researchers from predicting or influencing group assignments before they occur.
- Blinding and masking: Blinding (or masking) helps minimise bias by preventing participants, researchers, and outcome assessors from being aware of treatment assignments. Depending on the nature of the trial, blinding may be single-blind (participants are unaware), double-blind (participants and researchers are unaware), or triple-blind (participants, researchers, and outcome assessors are unaware).
- Statistical Analysis Plan (SAP): Before beginning data collection, it is essential to develop a detailed SAP. The SAP outlines the statistical methods that will be used to analyse the data, including primary and secondary endpoints, treatment comparisons, adjustments for covariates, handling of missing data, and methods for controlling Type I error rates (e.g. Bonferroni correction).
- Statistical hypothesis testing: Statistical hypothesis testing is used to assess whether observed differences between treatment groups are statistically significant or are likely due to chance. Common tests include t-tests, chi-square tests, analysis of variance (ANOVA) and regression analysis. It is important to pre-specify the primary hypothesis and statistical tests to control the overall Type I error rate.
- Interim analyses and data monitoring: Interim analyses may be conducted during the trial to assess for efficacy or safety concerns, but they must be carefully planned and adjusted for multiple comparisons to avoid inflated Type I error rates. Data Safety Monitoring Boards (DSMBs) may be established to oversee these analyses and make recommendations regarding trial continuation, modification, or termination. DSMBs are independent committees that regularly review accumulating data from the trial, with a focus on safety and efficacy. Their composition and operations must be approved by a Human Research Ethics Committee (HREC) as part of the ethical review process.
- Handling missing data: Missing data can compromise the validity and power of statistical analyses. Strategies for handling missing data should be outlined in the SAP. Such strategies might include multiple imputation, last observation carried forward (LOCF) or sensitivity analyses to assess the impact of missing data assumptions on study results.
- Regulatory requirements: Trials conducted in Australia must adhere to regulatory requirements set out by the TGA and other relevant authorities such as the NHMRC. This includes following guidelines for statistical analysis, data reporting and submission of trial results for regulatory approval.
Tools for capturing data
In Australia, there are a variety of Electronic Data Capture (EDC) systems available. It is important that, whatever tool is used, the system is compliant with regulatory standards, data security, and integrity.
REDCap
- Description: A secure, web-based application designed to support data capture for research studies, providing an intuitive interface for validated data entry.
- Features: Customisable forms, audit trails, data export options, and compliance with regulatory standards.
- Uses: Suitable for academic, non-profit, and government-sponsored clinical trials.
Qualtrics
- Description: A powerful and versatile cloud-based platform for capturing, managing, and analysing surveys and research data.
- Features: Highly customisable, real-time data access, secure data storage, and integration with other clinical systems.
- Uses: The platform is scalable, accommodating everything from small-scale academic research to large-scale, multi-national clinical trials.
Medidata Rave
- Description: A comprehensive EDC system that manages and streamlines the clinical trial process.
- Features: Advanced data management, real-time data access, integration with other clinical systems, and compliance with 21 CFR Part 11.
- Uses: Ideal for large-scale clinical trials conducted by pharmaceutical companies and CROs.
Oracle Clinical
- Description: An integrated clinical data management system that captures, manages, and reports clinical trial data.
- Features: Comprehensive data management, robust security, advanced reporting tools, and regulatory compliance.
- Uses: Suitable for complex clinical trials requiring extensive data management capabilities.
FAQs about data management
Data management in clinical trials ensures that trial data is collected, processed and analysed accurately, reliably and in compliance with regulatory requirements. It involves procedures for data collection, storage, retrieval and sharing throughout the lifecycle of the trial.
Regulatory requirements for data management in Australian clinical trials are outlined by the TGA and the NHMRC. These requirements include guidelines for data integrity, confidentiality and security, as well as standards for EDC systems and data-sharing practices.
Key considerations when developing a DMP include defining data collection tools and procedures, ensuring data quality and integrity, establishing protocols for data storage and security, addressing participant privacy and confidentiality, outlining data analysis methods, and planning for data sharing and dissemination.
Data should be collected using standardised, validated instruments and recorded accurately and legibly in accordance with the trial protocol. EDC systems are commonly used to streamline data collection and entry, minimising transcription errors and ensuring data quality.
Measures to ensure data integrity and security may include implementing access controls and user permissions, maintaining audit trails of data modifications, encrypting sensitive data, regularly backing up data, and complying with relevant privacy laws and regulations.
Adverse events and other safety-related data should be promptly collected, documented and reported in accordance with regulatory requirements and trial protocols. Data management procedures should include mechanisms for timely assessment, reporting and follow-up of safety events to ensure participant safety and regulatory compliance.
Steps may include cleaning or anonymising and validating the data, conducting statistical analyses according to the pre-specified analysis plan, interpreting the results in the context of the trial objectives, and preparing comprehensive reports for regulatory submissions and publication.
