A new article entitled "The causal inference paradigm for network meta-analysis with implications for feasibility and practice" is currently available (pre-peer review) on arXiv stat:
http://arxiv.org/abs/1506.01583
Network meta-analysis involves the aggregation of study results in order to contrast the effects of different treatments for a common condition. While standard meta-analysis only summarizes over one pair of treatment contrasts, network meta-analysis can combine a multitude of different treatments.
In this article, we define a nonparametric "effect of interest" in a network meta-analysis over heterogeneous underlying populations. We then develop a set of restrictions that allow for estimation of this effect. We propose several estimators and compare their realistic small-sample performance in a simulation study. Finally, we demonstrate this approach in a real data example to evaluate the relative effectiveness of antibiotics on methicillin-resistant Staphylococcus aureus (MRSA) infection.
http://arxiv.org/abs/1506.01583
Network meta-analysis involves the aggregation of study results in order to contrast the effects of different treatments for a common condition. While standard meta-analysis only summarizes over one pair of treatment contrasts, network meta-analysis can combine a multitude of different treatments.
In this article, we define a nonparametric "effect of interest" in a network meta-analysis over heterogeneous underlying populations. We then develop a set of restrictions that allow for estimation of this effect. We propose several estimators and compare their realistic small-sample performance in a simulation study. Finally, we demonstrate this approach in a real data example to evaluate the relative effectiveness of antibiotics on methicillin-resistant Staphylococcus aureus (MRSA) infection.
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