This article is published in Pharmaceutical Statistics as a special issue on Focusing on the PSI Special Interest Groups, edited by John Stevens, Centre for Bayesian Statistics in Health Economics, ScHARR, Regent Court, 30 Regent Street, Sheffield, South Yorkshire, S1 4DA, UK.
Statistical approaches for conducting network meta-analysis in drug development
Version of Record online: 28 DEC 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Special Issue: Focusing on the PSI Special Interest Groups
Volume 10, Issue 6, pages 523–531, November/December 2011
How to Cite
Jones, B., Roger, J., Lane, P. W., Lawton, A., Fletcher, C., Cappelleri, J. C., Tate, H., Moneuse, P. and on behalf of PSI Health Technology Special Interest Group, Evidence Synthesis sub-team (2011), Statistical approaches for conducting network meta-analysis in drug development. Pharmaceut. Statist., 10: 523–531. doi: 10.1002/pst.533
- Issue online: 28 DEC 2011
- Version of Record online: 28 DEC 2011
- network meta-analysis;
- health technology assessment;
- statistical methodology
We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS® software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity. Copyright © 2011 John Wiley & Sons, Ltd.