Study-centric data is not available by default for Cochrane reviews. If you are interested in learning more about study-centric data and how to start using it, please contact support@cochrane.org.

An overview

This page gives an overview of the differences between study-centric data analyses and custom input analyses. It also presents pros and cons of both approaches.

The main difference is:

  • In a custom-input analysis the study result data is entered and stored in the analysis data table. This is the traditional analysis type done in Review Manager 5.
  • In a study-centric data analysis the meta-analysis presented in the forest plot is pulled together from result data entered and stored in the included study. We call this structure 'study-centric data structure'.

Key benefits

  • Choosing to use study-centric data analyses in the review help authors think more systematically about how to structure their analysis earlier in the process. 
  • Enables a future Covidence integration that supports updates.
  • For sensitivity analyses where data is reused, study-centric data structure reduces the time to generate analyses and reduces the risk of data entry errors.
  • Long term, study-centric data structure will allow introduction of new statistical methods.

It is currently not possible to migrate all existing custom-input analyses to study-centric data analyses. Therefore a review can include both study-centric data analyses and custom-input analyses.


1. Where to enter study results?

In custom-input analyses, study results are entered in the data table of each analysis.

Study-centric data analysis is done in RevMan Web only. Study results are entered on the result data tab of an included study for each outcome, after adding the study arms of the study. 

2. How to set up an analysis?

In custom-input analysis, the study results are added for each outcome in a single data table.

In study-centric data structure, setting up an analysis is done after the interventions and outcomes of the review are set up in the PICO tab and after the result data is added in the studies.  To set up an analysis, one needs to choose outcome, experimental intervention and control intervention, and a possible subgroup factor. RevMan Web then identifies which studies have result data that matches the synthesis PICO and pools the results for the analysis. 

NOTE: It is not possible to Compare versions or View versions if the analysis synthesis PICO in one of the versions is not complete e.g. if you have not defined at least the Outcome, Experimental intervention and Control intervention.

3. Which tools support study-centric data?

Study-cenric data is only available in RevMan Web. When you have enabled the feature you will not be able to check out the review to RevMan 5 to edit it. 

4. Types of analyses available in the two approaches

This table presents the types of analyses available in two approaches and how they are set up.

Type of analyses

Custom input analyses

Study-centric data analyses

Analyses without subgroupsYesYes
Subgrouping by a property of the included studies
e.g by outcome measure - a study falls under one of the categories of the covariate
YesYes, set covariate categories for each study and choose subgroup factor in the synthesis PICO .
Subgroup by risk of biasYesNo, implementation planned. Use custom-input analysis for now.  
Combining armsYes, with the help of the calculator in RevMan 5.Yes, done automatically.
Splitting control arms
e.g. in analyses that subgroup by the type of intervention (e.g. low-dose vs high-dose),
sometimes the same control arm is used multiple times
Yes, compensate for this by "splitting" the control arm, so that the overall pooled estimate wouldn't be over-confident.Yes, the study will automatically be counted only once for the pooled estimate. But this is only relevant when subgrouping by intervention is implemented.
Create analyses with contrast data (called GIV in custom input analyses)Yes. Yes. 
Create analyses with different types of granularity of interventions (e.g. any antibiotic vs placebo and A specific antibiotic vs placebo) YesYes.
Subgroup by property of the intervention groups (arms)YesNo. Use custom-input analysis.  
Subgroup by variants of the outcomeYesNo. Use custom-input analysis.  

Subgroups within studies, e.g. subgrouping the participants of a study

YesNo, Use custom-input analysis.