<p>Network meta-analysis (NMA) is a process by which several treatments can be simultaneously compared for relative effectiveness. When conducted in a Bayesian framework, the probability that each treatment is ranked 1st, 2nd and so on can be calculated. A square matrix of these probabilities, referred to as the rank probability matrix, can be structured with rows representing treatments and columns representing ranks. In this thesis, a simulation study was conducted to explore properties of five proposed rank probability matrix summary measures: determinant, Frobenius norm, trace, diagonal maximum and diagonal minimum. Each measure is standardized to approach 1 for absolute certainty. The goal of this simulation is to identify strengths and weaknesses of these measures for varying networks. The measures are applied to previously published NMA data for further investigation. The simulation study and real data analysis revealed pros and cons of each summary measure; the Frobenius norm was found most effective. All summary measures yielded higher values with increases in symmetry, relative effect size and number of studies in the network. If the rank probability matrix is used as the primary output of a network meta-analysis (as is often the case), a simple measure of the overall confidence in the rankings is beneficial. Future research will require exploration into the distributions of these measures.</p> / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/15338 |
| Date | 13 February 2015 |
| Creators | Richer, Danielle M. |
| Contributors | Beyene, Joseph, Mathematics and Statistics |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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