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Statistical approaches for conducting network meta‐analysis in drug development
Author(s) -
Jones Byron,
Roger James,
Lane Peter W.,
Lawton Andy,
Fletcher Chrissie,
Cappelleri Joseph C.,
Tate Helen,
Moneuse Patrick
Publication year - 2011
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.533
Subject(s) - frequentist inference , computer science , bayesian probability , set (abstract data type) , random effects model , risk analysis (engineering) , meta analysis , data mining , machine learning , operations research , bayesian inference , artificial intelligence , mathematics , medicine , programming language
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.