Facilitate Open Science Training for European Research Clinical Trial Data Management Dr Evelyn Flanagan e.flanagan@ucc.ie Clinical Research Facility -UCC CLINICAL DATA MANAGEMENT OVERVIEW • Role of the Data Manager in Clinical trials • Paper Vs Electronic Data Capture • General understanding of the principles underpinning data management for clinical studies. • Overview of the data cycle in a clinical study. • Overview of data management plan. Role of Data Management in Clinical Trials Study Setup • CRF design and development • Database build and testing • Edit Checks preparation and testing Study Conduct • Data Entry • Discrepancy Management • Data review (Ongoing QC/QA) • Query Reconciliation (Ongoing) • Data Transfer Study Closeout • Query Reconciliation • Quality Control • Database Lock • Electronic Archival • Database Transfer Paper Vs Electronic Data Capture Traditionally, paper-based case report forms or questionnaires have been used for collecting data as part of clinical practice, research studies, and QC exercises Despite being simple, manual data entry into spreadsheets, and subsequent verification is time-consuming, tedious, and prone to data errors. EDC systems have emerged as an alternative to paper-based systems Why Electronic Data Capture ? Paper-Based Trials CRFs Investigator Data Manager Data Entry Queries/ DCFs CRA Project Manager Database Time Data availability Patient recruitment trends Query resolution Database lock Paper Studies – Problems Quality Data validation Patient eligibility Handwriting & transcription Cost Data input Document printing & distribution CRA travel Archiving Why Electronic Data Capture ? EDC Trials Investigator Database Data Manager CRA Project Manager Time Instant data availability Rapid trends reporting Quicker query resolution Faster database lock EDC Trials – Benefits Quality On-line data & patient validation No handwriting or transcription problems Protocol updated on-line Cost No repeat data input No document printing or distribution Less CRA travel Regulations/ Guidance REGULATORY FRAMEWORK Good clinical practice is an international ethical and scientific quality standard for the design, conduct and record of research involving humans. GCP is composed of 13 core principles, of which the following 2 applies specifically to data. GCP – CORE PRINCIPLES FOR DATA •The confidentiality of records that could identify subjects should be protected, respecting the privacy and confidentiality rules in accordance with the applicable regulatory requirement(s). • All clinical trial information should be recorded, handled, and stored in a way that allows its accurate reporting, interpretation and verification. 21 CFR Part 11 • Validation • Record generation and copying • Record protection • Access • Audit Trails • Operational system checks • Authority checks • Device/Terminal Checks • Training/User accountability • System document control • Controls for open systems • Electronic Signatures DATA SEQUENCE • A case report form (CRF) is a printed or electronic form used in a trial to record information about the participant as identified by the study protocol. • CRFs allow us to: – record data in a manner that is both efficient and accurate. – Record data in a manner that is suitable for processing, analysis and reporting. WHAT IS A CRF? Designing CRFs, key questions: • What data is required to be collected? – Only data we specified in the proposal/protocol. – Only data required to answer the study question. • When will this data be collected? – Baseline / follow-up . • What Forms will need to be designed. • Who is going to collect/complete this form. • Are there validated instruments available? • How is the data going to be analysed. KEY QUESTIONS DATA SEQUENCE Metadata is structured data to organise and describe the data being collected. It is a tool to control and maintain data entities: – Content and variable definitions – Validation rules •Metadata consistently and effectively describes data and reduces the probability of the introduction of errors in the data framework by defining the content and structure of the target data. WHAT IS METADATA? Metadata File Name of Trial/Study: PAAD (Probiotics for Antibiotic Associated Diarrhoea) - stage 1 Metadata Author: H S Number of Data Collection Forms for Trial/Study: 10 Name of File (Corresponding Data Collection Form): Recruitment CRF 02 Form Variable Variable Label Data Type Format Length Linked Skip Validation Validation Title Name Value Missing Condition Type Labels Codes Recruitment CRF 02 datecons date of consent date dd/mm/yyyy 10 range warn if <01.11.2010 > 01.06.2012 sugender service user gender category 1 = Male, 2 = Female 1 consss1 consent for SS1 category 0 = no; 1 = yes 1 CRF AND DATABASE DESIGN • Study outcomes in protocol define what questions are asked in the CRF. • Use of validated scales and questionnaires. •User-friendliness and ease of completion important. • Database is built to receive data extracted from the CRFs. • Database needs to include querying and reporting tools. •Data needs to be coded into numbers to facilitate statistical analysis. DATABASE DESIGN Database allows for adequate storage of study data and for accurate reporting, interpretation and verification of the data. 2 database systems tend to co-exist alongside one another: • Study management database: recruitment, data completeness (CRF receipts) follow-up triggers… • Clinical database: clinical information (study outcomes). DATABASE DESIGN Functionalities to consider in both types of database: • Validation rules (Ranges, skips, inconsistencies…). • Queries / report. • Audit trail. Clinical Data Integration • Increase efficiencies of performing clinical trials • Streamline data collection at investigator sites • Higher quality data due to consistency of requirements • Allow software development against a common standard • Provide long-term means for electronic data archive • Facilitate review of submissions The Key Role of Data Standards DATA SEQUENCE DATA COLLECTION • Validity of data collection must be ensured. • Source data is identified and data transcribed correctly onto data collection system. • Process of data collection/transcription is audited throughout the process (monitoring – Source data verification). DATA COLLECTION • Before starting data collection – Testing – SOP – Training • During data collection – Monitoring/Audit Testing the system • Test • Re- Test • Re-Test again DATA COLLECTION TRAINING • After piloting, when it is working as it should, next step is to train all users of the system • A record should be kept of the training • A detailed diagram and description of how data will be collected should be provided at training. DATA COLLECTION AUDIT • Maintain an audit trail of data changes made in the system. • Procedure in place for when a study participant or other operator capturing data, realises that he / she has made a mistake and wants to correct data. • Important that original entries are visible or accessible to ensure the changes are traceable. ELECTRONIC DATA COLLECTION SPECIFIC TRAINING ISSUES • Training on the importance of security; including the need to protect passwords, as well as enforcement of security systems and processes. • Maintain a list of individuals who are authorised to access data capture system. • Ensure that the system can record which user is logged in and when. Timely removal of access no longer required, or no longer permitted. Data Entry Process DATA SEQUENCE Don’t over estimate your reader’s abilities DATA ENTRY • Different types of data entry exist, (manual /optical mark recognition system, online/offline, etc…). • Type of data can also influence the method of data entry (numerical, free text, images etc…). • It is important to have documented procedures (SOPs) defining who is performing data entry and how it is performed. • Data entry procedures should be tested at the earlier design stage, and testing adequately documented before sign-off. •Adequate training on these procedures should be provided. Userguides/Data dictionary/Online recording •Appropriate quality control procedures have to be set up. DATA ENTRY AFTER DATA COLLECTION •Regular backups should be made of your data, if outsourcing data collection or storage ensure that the company have backup systems in place. •May need to archive whatever data you collect, includes both hard copy and electronic data, documents not archived need to be disposed of securely. Putting the pieces together Photo by Dread Pirate Jeff http://www.flickr.com/photos/ justageek/2851643792 Data Management Plans (DMPs) Importance of a DMP • Make informed decisions to anticipate and avoid problems • Avoid duplication, data loss and security breaches • Develop procedures early on for consistency • Ensure data are accurate, complete, reliable and secure • Save time and effort – make your life easier! Funders’ DM expectations DMP- Five common themes 1. Description of data to be collected / created (i.e. content, type, format….) 2. Standards / methodologies for data collection & management/ Short term storage 3. Ethics and Regulations 4. Plans for data sharing and access (i.e. how, when, to whom) 5. Strategy for long-term preservation Thank you!