Implementation Science Workshop Ensuring High Quality Data 28 October 2010 Day Munatsi - Chief Data Manager Centre for the AIDS Programme of Research in South Africa
Agenda Data Quality – Definition Quality Assurance Aspects Quality Management Considerations Quality Planning Quality Control Quality Improvement Further Analysis Summary
Data Quality - Definition The degree of excellence exhibited by the data in relation to the portrayal of the actual scenario. The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use. The processes and technologies involved in ensuring the conformance of data values to project requirements and acceptance criteria
Quality Assurance "A task well done is its own reward.” Ralph Waldo Emerson
Quality Assurance Quality assurance (QA) is the prevention, detection, and correction of errors or problems QA is closely tied to regulatory compliance Good practice must be closely tied to following regulations.
Quality Planning Make use of : Data Management Plan Standard Operating Procedures (SOPs) Best Practices (Guidelines)
Quality Planning What data collection instrument will be used ? Where will we store the data ? Who will perform data entry? Training? On-line help? How data entry will be performed?
Where do we start? Paper Data Collection Options Case Report Forms (CRFs) e.g. questionnaire, data collection forms Patient forms Existing data sources Databases e.g. MS Access, Epi Data, DataFax, OpenClinica Spreadsheets e.g. MS Excel
Database Design Consideration…1 Hidden text fields, where text appears along with numeric values. E.g. 10-15 , <22 Dates of all kinds E.g. “6/-/95” for June 1995 , mm/dd/yyyy Vs dd/mm/yyy Text fields and annotations E.g. Categorical (coded) values, Short comments, Reported terms, Long comments.
Database Design Consideration…2 Header information E.g. PID, Visit Date, Visit Code, Study ID Single check boxes E.g. Check if any adverse events: [ ] Calculated or derived values E.g. age ,number of days on treatment, weight in kilograms
Dates – Special Note : Dates on a CRF typically fall into three categories: Known dates related to the study (visit date, lab sample date) Historical dates (previous surgery, prior treatment) Dates closely related to the study but provided by the patient (concomitant medication, adverse events [AEs])
Other Considerations Avoid repetition Identify a unique identifier or primary key Ensure that individual column values are valid Follow “Do and Review” policy
Data entry considerations….1 Define “must enter” fields – No record is complete unless: such and such is entered; Define “skip patterns” – If answer on field 1 is ‘No’ then jump to field 5. Define “edit checks” If Date Of Birth is inconsistent with today’s date , raise a flag Make data entry fool proof. Grade level can be entered as a number (4 or 4th or four). By using a pull-down menu with the correct data format these mistakes can be avoided.
Data entry considerations….1 Have at least 2 levels of data validation if possible. Double Data Entry, Define missing value codes D for Not Done , U for Unknown, A for Not Applicable