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A simple formula for enumerating comparisons in trials and network meta-analysis.

Farhad Shokraneh¹, Clive E Adams¹

¹Cochrane Schizophrenia Group, Division of Psychiatry and Applied Psychology, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham, NG7 2TU, UK.

|April 19, 2019

Summary

A new formula accurately calculates potential intervention comparisons in meta-analyses. This tool enhances reporting transparency by comparing actual versus predicted pairwise comparisons, aiding researchers in network meta-analysis studies.

Keywords:

Clinical Trials Network Meta-Analysis Pairwise Comparisons Randomised Controlled Trials Study-Based Registers Systematic Reviews

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Area of Science:

Medical Statistics
Evidence Synthesis

Background:

Network meta-analysis (NMA) synthesizes evidence from multiple treatment comparisons.
Accurate enumeration of potential pairwise comparisons is crucial for NMA transparency and completeness.

Purpose of the Study:

To introduce and validate a simple formula for calculating the total number of potential pairwise comparisons in single trials and NMAs.
To assess the formula's accuracy against enumerated direct comparisons and reported indirect/direct comparisons in an existing NMA.

Main Methods:

A formula was developed to predict pairwise comparisons.
Data from a prior NMA (133 trials, 8 interventions) was used to build a trial register.
Direct comparisons were enumerated, and the formula's predictions were compared with these and reported NMA comparisons.

Main Results:

The NMA involved 16 unique direct comparisons among 8 interventions across 133 trials.
The developed formula accurately predicted 28 potential indirect or direct comparisons.
The predicted number of comparisons matched the number reported in the NMA.

Conclusions:

The proposed formula accurately enumerates potential pairwise comparisons in single trials and NMAs.
Implementing this formula can improve transparency in reporting, highlighting discrepancies between actual and potential comparisons.
This method aids in identifying potential shortfalls in evidence synthesis.