Meta-Analysis of Migraine Headache Treatments: Combining Information From Heterogeneous Designs

Migraine headache is a common condition in the United States for which a wide range of drug and nondrug treatments are available. There is wide disagreement about which treatments are most effective; meta-analysis of existing clinical trials can help to bring existing evidence to bear on this question. Conducting a meta-analysis is a challenging statistical problem because of the absence of a uniform accepted definition of headache syndromes, the diversity of treatments, and the heterogeneous and incomplete nature of published information. The results of studies are summarized in various ways; most studies report continuous treatment effects for each treatment, but some report only differences in effectiveness for pairs of treatments, and others report only 2 × 2 contingency tables for dichotomized responses. In this article we present a hierarchical Bayesian grouped random-effects model for synthesizing evidence from several clinical trials comparing the effectiveness of commonly recommended prophylactic treatments for migraine headaches. We incorporate explicitly the relationships among the different classes of treatments and introduce latent auxiliary variables to create a common scale for combining information from studies that report results in very different forms. This model permits us to synthesize this heterogeneous information and to make inferences about treatment effects and the relative ranks of treatment without understating uncertainty. Estimation, ranking, model validation, and sensitivity analysis are all implemented through simulation-based methods.