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Edit check specification

 Most important goal of a Clinical Data Manager is to ensure the consistency, quality & accuracy of clinical data. In this journey Edit check specification (ECS)plays a very crucial role to enable data cleaning.

ECS is created by DM’s with the input of study team member in clinical database build stage.

3 crucial things to understand about ECS

1. What are the pre-requisites to create / build ECS?
2. What are the checks to include in ECS?
3. What to avoid while writing ECS?


⭐ 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 / 𝗯𝘂𝗶𝗹𝗱 𝗘𝗖𝗦: To build an ECS that severs the CDM needs,

- One should have clear understanding of study protocol & eCRF data points collected.

- Data dictionary of the database (EDC) - this helps to understand the various attributes, definitions of the elements that are being used in database.

- Possible data entry scenarios & test data: This will help to create a correct edit checks to address all the possible data issues that can happen when study go live.

- List of critical data points such as study end points, AE / SAE data etc.

⭐ 𝗖𝗵𝗲𝗰𝗸𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝗱 𝗶𝗻 𝗘𝗖𝗦?

Based on the type, checks in ECS can be categorized into

a. Univariate checks (these checks only applicable to a single field or single variable ona eCRF.)

b. Multi-variate Checks (edit checks with more than one fields or variables involved.)

c. Programmed Checks (they will fire as soon wrong data is entered)

d. Manual Checks ( need to run manual programs on the data & output reports are analyzed for data issues)

Based on core function – checks that are included in ECS can be categorized into

1. Missing data & eCRF checks : these checks ensure all the critical data points & eCRF’s are collected as needed (Ex : Missing inform consent date / missing Vital sign forms etc

2. Range Checks & format checks: they raises a query when entered value is out of range or format - specified for data collection point (ex: height, weight dates, time etc

3. Checks for Duplicate data: These checks alerts sites / DM when duplicate data is entered. Ex: Entering same AE event twice

4. Protocol deviation checks : These are must have checks to flag PD’s
   
5. External Data Checks : These check will ensure data quality of external data such as labs data, ECG etc
   
6. Inconsistency across single CRF & other eCRF’s : These will check inconsistencies on the same eCRF form across eCRF forms.

⭐ 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱 𝘄𝗵𝗶𝗹𝗲 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗘𝗖𝗦?

1. Avoid complicate queries messages – this will hurt sites

2. Avoid placing too many edit checks in a form or visit

3. Avoid leading /probing/ or guiding participants enter any data point in a particular way.

4. Do not blindly copy checks from other / sister studies.

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