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Updated: May 26, 2026
05:10
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Statistical approaches for conducting network meta-analysis in drug development.
Byron Jones1, James Roger, Peter W Lane
1Novartis, Basel, Switzerland. byron.jones@novartis.com
Pharmaceutical Statistics
|January 4, 2012
Summary
Network meta-analysis (NMA) statistically combines direct and indirect evidence for health technology assessment. This guide details NMA methods, including random-effects models and Bayesian approaches, with SAS code examples.
Area of Science:
- Health Technology Assessment
- Evidence Synthesis
- Biostatistics
Background:
- Network meta-analysis (NMA) extends standard meta-analysis by integrating direct and indirect treatment comparisons.
- NMA is crucial for evaluating new health technologies during development and approval processes.
Purpose of the Study:
- To detail statistical approaches for conducting network meta-analysis (NMA).
- To provide practical guidance and SAS code for implementing NMA methods.
- To address overlooked aspects of NMA, such as random-effects constraints.
Main Methods:
- Illustrates NMA using fixed-effects and random-effects models.
- Compares frequentist and Bayesian statistical approaches for NMA.
- Demonstrates NMA with SAS code examples for pharmaceutical industry statisticians.
Main Results:
- Explains NMA as analogous to analyzing incomplete-block designs.
- Highlights the impact of random-effects constraints on NMA estimates and standard errors.
- Proposes symmetric constraints for random effects in NMA.
Conclusions:
- Emphasizes the role of statisticians in planning and executing NMAs.
- Discusses strategies for managing heterogeneity in NMA.
- Provides a comprehensive overview of NMA for health technology assessment.
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