Common Data Element: BRICS data completeness index
Listed below are the details for the data element.
FITBIR
1.0
Element Type
Common Data Element
BRICS data completeness index
BRICSDataCompletIndex
Short Description
Index reflecting the completeness of data in BRICS calculated for a given row as % of missing variables. Calculated in %
Definition
Index reflecting the completeness of data in BRICS calculated for a given row as % of missing variables. Calculated in %
Notes
Creation Date
2019-06-11
Historical Notes
References
Schafer JL. Multiple imputation: a primer. Stat Methods in Med. 1999;8(1):3�15. doi: 10.1191/096228099671525676. Bennett DA. How can I deal with missing data in my study? Aust N Z J Public Health. 2001;25(5):464�469. Tabachnick BG, Fidell LS. Using multivariate statistics. 6. Needham Heights, MA: Allyn & Bacon; 2012.
Data Type
Numeric Values
Input Restrictions
Free-Form Entry
Minimum Value
0
Maximum Value
100
Population
Adult and Pediatric
Guidelines/Instructions
Calculate (in %) how many data points were submitted per row (dataset completeness % or DC), as DC=[(total number of questions(variables) in the dataset - number of questions/variables with data)/total number of questions/variables in the dataset]*100, where "total number of questions/variables in the dataset" is the number of variables for raw questions (scores are not included). E.g. In PHQ9 it equals 9. Then calculate DCI as the % of questions/variables missing. Dataset Completeness Index (DCI)=100-DC,
Preferred Question Text
BRICS data completeness index
Category Groups and Classifications
| Disease | Domain | Sub-Domain |
|---|---|---|
| General (For all diseases) | Outcomes and End Points | Other Clinical Data |
Classification
General (For all diseases):
Supplemental
Keywords
Data
Completeness
Core
Labels
