Data Management in Clinical Research Rosanne M. Pogash, MPA Manager, PHS Data Management Unit January 12, Data Management Topics l Data collection instruments l Common data elements/standardization l Data dictionary or codebook l Database structure l Data capture l Data entry l Data verification l Data quality l Data errors l Data error resolution l Audit trail l Data quality monitoring l Data integrity l Participant confidentiality/privacy l Database security l Data audits l External data transfers l Data sharing l Data preservation Presentation Focus. Definition of Data Quality Institute of Medicine Data that are fit for use Data that give the same result as error-free data In the context of a research study Data that accurately represent the measurements included in the statistical analysis plan Team Approach to Research Design Quality Research Data Principal Investigator(s) Content Experts Statistician Research Coordinator Data ManagerIT Support Regulatory Expert Human Subjects Protection Expert Public Health Sciences Contribution Quality Research Data Principal Investigator(s) Content Experts Statistician Research Coordinator Data ManagerIT Support Regulatory Expert Human Subjects Protection Expert Sources of Research Data Medical Records Physiological Measurements Research Database Personal Devices Scanners/Fax Machines Forms/Surveys Audio/Video Recordings Interactive Voice Recognition (IVRS) Computer Assisted Telephone Interview (CATI) Face-to-face Interviews Other Databases Research Team Improve Data Quality Prior to data collection At the point of data entry After data entry Improve Data Quality Prior to data collection At the point of data entry After data entry Prior to Data Collection l Define the data type for each variable n Categorical - defined response choices n Numeric n Date/Time n Text n Unique formats u Telephone number u Zip code u Prior to Data Collection l Minimize the number of variables allowing free text responses n Cannot be analyzed n Will need to be coded prior to analysis n Appropriate to describe Other selected from a list of responses Prior to Data Collection l Define the format n Numeric fields u Integer u Number with one decimal place u Number with two decimal places, etc. n Date fields u Month/Day/Year u Day/Month/Year u Year/Month/Day Prior to Data Collection l Define the format n Time fields u 12-hour clock with AM and PM designations u 24-hour clock u HH:MM u MM:SS u HH:MM:SS Prior to Data Collection l Define the units of a numeric value n pounds versus kilograms n inches versus centimeters n g/dL n mg/dL n U/L n mmHg Prior to Data Collection l Code categorical variables n Ordinal represents degree of measurement u 1 = Mild u 2 = Moderate u 3 = Severe u 1 = Strongly Disagree u 2 = Disagree u 3 = Agree u 4 = Strongly Agree Prior to Data Collection l Code categorical variables n Nominal - arbitrary; does not represent degree of measurement u 0 = No u 1 = Yes u 9 = Dont know u 1 = Male u 2 = Female n Be consistent throughout the study by using the same codes Improve Data Quality Prior to data collection At the point of data entry After data entry At the point of data entry l Choose a software application where you can minimize data entry errors by allowing you to: n Define the data type n Define the format n Control permissible values u Defined codes u Ranges for numeric variables At the point of data entry l Microsoft Excel is an acceptable option ONLY if you use features which can minimize data entry errors n Cell formatting n Data validation n Freeze panes l Microsoft Excel lacks n Ability for multiple users to enter data at the same time n Adequate audit trail recording who and when edits are made n Access security n Role-based access Excel Data Validation Excel Cell Formatting At the point of data entry l Research Electronic Data Capture application l Developed by CTSA-supported group at Vanderbilt University l Penn State has its own license l Maintained on HMC server l Free of charge to use l Used by over 1700 institutions in 96 countries l Build electronic data collection forms with variable validation, branching logic, calculated fields l Create a study database structure l Create and distribute surveys l Import electronic data from other sources l Export data to common data analysis packages l Review an audit trail of every action completed in the study database l Create and view reports l Store study related documents l Create and execute data quality checks l Resolve data errors across individuals l View a graphical representation of data l Control rights based on roles in study Creating Forms in REDCap Improve Data Quality Prior to data collection At the point of data entry After data entry l Verify that the data entry was completed accurately n Perform double data entry and compare the two entries for inconsistencies u REDCap has a double data entry option with a data comparison tool n Visually audit the electronic data by selecting a random sample of records After data entry l Create rules to identify potential data errors n Missing values n Out-of-range values n Problems with branching logic n Illogical/inconsistent data After data entry l REDCap has 7 pre-defined data quality rules that you can execute following data entry. n Missing values (excluding missing values due to branching logic) n Missing values for required fields only n Incorrect data type n Out-of-range values n Outliers for numerical fields n Hidden fields that contain values n Multiple choice fields with invalid values l You can create customized rules as well. Example of a Study Design in REDCap PHS Data Management Services l Develop data management plans l Design data collection forms l Design administrative forms to facilitate data collection and protocol adherence l Provide design assistance to investigators/coordinators creating REDCap projects l Create REDCap projects forms, surveys, and data quality rules l Create and implement testing plans for REDCap designs and data quality rules l Perform data entry l Perform data monitoring activities PHS Data Management Services l Up to 10 hours of services may be free using funds supported by the CTSI l Contact: n Rosanne Pogash Manager, PHS Data Management Unit nn