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Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.

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Chronic fatigue syndrome constitutes a major public health problem. Longitudinal follow-up of this cohort will be used to further evaluate the natural history of this illness.

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Information provided to patients is thought to influence placebo and drug effects. We investigated the potential relationship between treatment labeling and its outcome in a prospective, within-subjects, repeated measures study of episodic migraine. A cohort of 66 participants documented 7 separate migraine attack: one untreated attack, followed by six attacks that were randomly assigned for either rizatriptan (10 mg Maxalt) or placebo treatments, each of which labeled once as ‘Maxalt’, once as ‘Placebo’, and once as ‘Maxalt or Placebo’ (459 documented attacks). Data were analyzed using generalized linear mixed model statistics. While Maxalt was generally superior to placebo, the placebo effect, and to a lesser extent Maxalt efficacy, increased monotonically with treatment labeling as follows: ‘Placebo’ label < ‘Maxalt or placebo’ label ≤ ‘Maxalt’ label. Efficacy of Maxalt mislabeled as placebo was not significantly different from the efficacy of placebo mislabeled as Maxalt. The placebo effect was significant under each labeling condition relative to no treatment, amounting in magnitude to >50% of Maxalt effect under the corresponding labeling condition. Thus, incremental “positive” information yielded incremental efficacy of placebo and medication during migraine attacks.

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