Algorithmic parameterization of mixed treatment comparisons
Mixed Treatment Comparisons (MTCs) enable the simultaneous meta-analysis (data pooling) of networks of clinical trials comparing ≥2 alternative treatments. Inconsistency models are critical in MTC to assess the overall consistency between evidence sources. Only in the absence of considerable inconsistency can the results of an MTC (consistency) model be trusted. However, inconsistency model specification is non-trivial when multi-arm trials are present in the evidence structure. In this paper, we define the parameterization problem for inconsistency models in mathematical terms and provide an algorithm for the generation of inconsistency models. We evaluate running-time of the algorithm by generating models for 15 published evidence structures.
|Keywords||algorithm, evidence consistency, indirect comparisons, mixed treatment comparison, model generation, network meta-analysis|
|Note||Online First, 20 September 2011|
|Persistent URL||dx.doi.org/10.1007/s11222-011-9281-9, hdl.handle.net/1765/30953|
van Valkenhoef, G., Tervonen, T., & de Brock, B.. (2011). Algorithmic parameterization of mixed treatment comparisons . Statistics and Computing, 1–13. doi:10.1007/s11222-011-9281-9