Data management for funding applications. RDCS training, 17 th March, 2014

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1 Data management for funding applications RDCS training, 17 th March, 2014

2 Overview of this session: The data cycle in a clinical study What is data management Data management procedures Data management considerations when applying for funding

3 Good Clinical Practice core principles for data handling: 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.

4 Data Cycle in a clinical study

5 What is data management? Thinking about data collection Can adequately answer your research question Is valid i.e. represents what you intend it to represent Can be analysed appropriately

6 What is data management? Data collection processes Ensure that data is only collected in the intended population / individual / participant Data is logged and stored appropriately Reliability of data verification

7 What is data management? Getting ready for analysis Prior to analysis data cleaning Data decisions made during study should be clearly communicated to statisticians

8 Data management procedures 1. Data collection Clinical Research Forms (CRFs) Retrospective data vs prospective data collection Paper vs electronic data capture Different data collection forms for different types of data Who is completing the form? Provision of data forms/mode of data return

9 Data management procedures 2. Metadata Describes the data being collected: Form Variable Category Format Title Name Selection Variable Label Data Type Value Missing Labels Codes 0, 1, 2, 3, 4, 5 pid Patient ID Integer <1 and >701 0, 1, 2, 3, 4, 5 pinit Participants initials Integer , 1, 2, 3, 4, 5 birthdt Participants date of birth 0, 1, 2, 3, 4, 5 health Have you ever been diagnosed with any of the following health conditions? health1 health1 health1 health2 health2 health2 Heart disease 0 No 1 Yes Diabetes 0 No 1 Yes Length Validation Date dd/mm/yyyy -9 8 <10/11/1959 and >10/11/1995 Category health1'=heart Disease; -9 'health2'=diabetes; 'health3'=depression; 'health4'=stroke; 'health5'=arthritis; 'health6'=hypertension; 'health7'=high Cholesterol; 'health8'=asthma; 'health9'=copd; 'health10'=chronic Backache; 'health11'=postnatal Depression; 'health12'=other

10 Data management procedures 2. Metadata Metadata tool to guide and maintain data entities Provides database developers/statisticians with static variable definitions throughout the study Reduces introduction of errors in the data framework defines the content and structure of the target data Once data collection has commenced metadata should NOT be amended

11 Data management procedures 3. Data tracking and storage Study management database/spreadsheet MS Access/Excel - What data has been collected on whom? - Feedback to funders re: recruitment etc. Clinical data storage in SQL? - Best practice safe and secure/easily auditable (MHRA) - Clinical Trials Registration - Adoption by registered trials unit

12 Data management procedures 3. Data tracking and storage Clinical database development requirements Clinical database development liaison with database developer Data entry directly into database vs uploading data files into database

13 Data management procedures 4. Validating your metadata/data why? Ensures that data responses match the intended variable label (i.e. the CRF question) = meaningful data Data analysed = data the team agreed would be analysed! Each data response has its own metadata label that is universal across SQL, Excel, SPSS etc.

14 Data management procedures 5. Data cleaning Querying questionable/invalid responses Removing invalid responses from datasets where indicated Decisions about data responses that do not match metadata Data cleaning ensure a more meaningful dataset

15 Data management considerations when applying for funding Is a data manager needed on your study? Paper based data collection design Database requirement / liaison with developers Site/data collection training (best practice) Reporting

16 Data management considerations when applying for funding Impact/cost of not having DM on a study Funder s expectations: participant recruitment and data collection Insufficient focus on participant follow-up and engagement Clinical trials: MHRA inspections

17 Data management considerations when applying for funding Impact/cost of not having DM on a study Protection of participants confidentiality and privacy Familiarity with study data and liaison with statisticians Risk of poor quality data: confidence in ability to answer research question (impact)

18 Data management considerations when applying for funding Data management advice when applying for funding Method of data collection feasible within research setting Type/quantity of data collection Advice on best practice SOPs/working guidelines

19 Data management considerations when applying for funding Data management advice when applying for funding Study set-up: Ethics, CRF design, metadata, database design Recruitment and follow-up: tracking, reminders, participant/site engagement, progress reporting End of study: data cleaning, liaison between sites / statisticians / developers

20 Conclusions Level of data management required can depend on: - Study design - Stage of study Data manager = go to person for in-depth information about the study data Service management in context of ability to adequately answer research question

21 Final considerations. Tasks required to ensure the quality of data do not go away - are these tasks suitable/appropriate for other team members? - time constraints of senior trial manager / trial manager / administrator Data managers focus on the broader aims of the study from set-up to analysis The reliability and validity of your results are a reflection of the data that is collected

22 Questions?

Data Management Dr Evelyn Flanagan

Data Management Dr Evelyn Flanagan e.flanagan@ucc.ie DATA MANAGEMENT OVERVIEW Role of the Data Manager in Clinical Trials General understanding of the principles underpinning data management for clinical