Abstract

Indirect comparisons and network meta-analysis are being seen increasingly often in cost-effectiveness analyses, reimbursement decisions, and academic journals. In essence, they allow investigators to draw coherent conclusions about the comparative efficacy of any number of treatments, based on evidence from randomized trials, which normally compare only two or three treatments. Statistical methods for network meta-analysis [1] go back to Gleser and Olkin [2], Higgins and Whitehead [3], and Hasselblad [4]. The idea had appeared earlier in the Confidence Profile Method literature [5], but not in a form compatible with the accepted principles of meta-analysis. The two-part report [6,7] from ISPOR's Task Force on Indirect Comparisons seems to represent the first position statement from an academic body on these methods. Prepared by a group drawn from major consultancy companies, the pharmaceutical industry, and academia, they provide a strong but balanced endorsement of the methods, particularly the Bayesian forms of analysis that fit conveniently within the probabilistic decision-modeling framework. At the same time, the report presses for more research to extend the methods into further areas of secondary analysis, such as synthesis of multiple correlated outcomes and to covariate adjustment, or meta-regression, while recognizing the difficulties of reliable covariate adjustment in the sparse data sets usually available.

ISPOR's position contrasts with the more cautious approach evident in the methodology guidelines published by some of the reimbursement authorities. The UK's National Institute for Health and Clinical Excellence (NICE) [8] made “direct evidence” the base case in appraisals of new technologies but allowed combination of “direct” and “indirect” as a secondary analysis. In Australia, the Pharmaceutical Benefits Advisory Committee makes limited use of network synthesis because its procedures encourage identification of a single comparator [9]. The Canadian Agency for Drugs and Technologies in Health [[10] also adopts a cautious stance. The 2008 Cochrane Handbook [11] avers that “indirect comparisons may suffer the biases of observational studies” and advises that direct and indirect evidence should only be combined as a supplemental analysis.