Study centric data management

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Study centric data management is Cochrane’s recommended approach to data management in intervention reviews (since April 2023). 

With study centric data management, study results data are entered and stored at the study level, rather than at the analysis level. This means that study results data are stored in one place and can be used in different analyses.

Study centric data management enables authors to work smarter in RevMan. Authors invest time when preparing the protocol to streamline the later analysis stages. This facilitates better defined and more focused reviews and a more efficient and robust data management process, and results in data that’s more readily re-usable. 

What are the key benefits of using study centric data management?

• Managing your study data efficiently helps you to think more systematically about how to structure analyses earlier in the process.

• Study results data are added in one place in RevMan and can be re-used for different analyses, including subgroup and sensitivity analyses, which saves time when generating analyses and reduces the risk of data entry errors.

• Easy transfer into RevMan of included study characteristics, results data and risk of bias assessments through imports.

• Automatic transformation of data from arm level to contrast level data for specific analyses, where applicable. See Populate study data for an explanation of these different types of data.

• Facilitates a new downloadable data package associated with published Cochrane reviews to increase impact, open opportunities for collaboration, reduce research waste, and make systematic reviewing more efficient. Study centric data makes it easier for users of the evidence to re-use in their own context. 

Getting started with study centric data management

Using study centric data management breaks down into several steps, some of which you can complete while working on your protocol, and others once you’ve identified the studies for inclusion in your review. The following knowledge base pages go into the detail of each step:

1. Define the Review criteria: set up the interventions and outcomes you will analyse, as well as anything else you need, for example to define subgroups.
2. Pre-define analyses: by defining your analyses at this early stage, before you’ve extracted study data, you make sure you’ve got your review criteria right.
3. Extract study data: this step is not done in RevMan, but can be completed in Covidence or another tool of your choice.
4. Populate study data: import data in CSV format (created using Covidence, or by completing the provided templates, or manually enter the study data).
5. Check your analyses: now that the study data has been populated, the relevant studies have automatically been included in your analyses.

You may want to go through some of these steps in a practice review to build a more complete understanding of the process. 

Methods resources

Core methods underpinning study centric data management structure are included in chapter 2 and chapter 3 of the Cochrane Handbook for Systematic Reviews of Interventions and the Intervention Synthesis Questions (InSynQ) checklist. Use these resources to define your criteria for including studies (PICO criteria for the review) and for how studies will be grouped for synthesis (PICO criteria for synthesis questions).

View these webinars for more background information and practical demonstrations:

• How study centric data in RevMan streamlines systematic review production 
• Study centric data analysis and data management in RevMan: methodological background and practical application

When should I use study centric data management?

RevMan supports two options for data management in intervention reviews:  

1. Study centric data management (recommended): Study result data is entered and stored at the study level (i.e. results data is stored in one place, study level, and used in different analyses). 
2. Manual input data management (optional): Study result data is entered and stored at the analysis level. You will need to enter all data manually.  

Both options can be used within the same review. Due to the advantages of study centric data for both review authors and users of the evidence, study centric data should be used whenever possible, and manual-input analyses should only be used when it is not possible to use a study centric analysis.

The following table outlines in which analysis scenarios study centric data can be used: