IN ONE SIMPLE SENTENCE
Designing the eCRF and collecting, checking and cleaning clinical data.
What is this service?
Raw study data must be organized, traceable, reviewable and ready for analysis. Data management turns scattered site entries into a clean and reliable dataset.
GOAL
Ensure the analysis dataset is not undermined by missing, duplicate, inconsistent or untraceable information.
What can MICT do here?
Design eCRFs, define data checks, review missing data, manage queries, clean the dataset and prepare the final database for analysis.
TYPICAL INPUTS
What we usually need to start
- Protocol / CIP
- Study variable list
- Site and participant data
- Logical rules and acceptable ranges
TYPICAL OUTPUTS
What this stage can deliver
- eCRF
- Data Management Plan
- Query and correction log
- Cleaned dataset
- Database lock / final analysis dataset
Important terms in plain language
eCRF
Electronic case report form used to record participant data.
Data Cleaning
Finding and resolving errors and inconsistencies in the data.
Query
A question sent back to a study site to clarify or correct a data point.
Missing Data
Required information that was not recorded.
Database Lock
The point when the final dataset is frozen and changes require formal control.
SIMPLE EXAMPLE — BLOOD PRESSURE MONITOR
If a participant is recorded as age 25 but the date of birth indicates age 65, the system should flag the inconsistency for review.