Statistical approaches for conducting network meta-analysis in drug development.
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.
Novartis, Basel, Switzerland. byron.jones@novartis.com
Byron
B
Jones
James
J
Roger
Peter W
PW
Lane
Andy
A
Lawton
Chrissie
C
Fletcher
Joseph C
JC
Cappelleri
Helen
H
Tate
Patrick
P
Moneuse
D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D055808:Drug Discovery / Q000706:statistics & numerical data*; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D013673:Technology Assessment, Biomedical / Q000706:statistics & numerical data*