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Simulation and data-generation for random-effects network meta-analysis of binary outcome.

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The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data-generating models (DGMs) are restricted to either… Click to show full abstract

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data-generating models (DGMs) are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multiarm trials with binary outcome. The only one of the common DGMs used in the pairwise case, which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

Keywords: network; random; simulation; network meta; meta analysis

Journal Title: Statistics in medicine
Year Published: 2019

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