Statistical methodologies for network meta-analysis with applications in veterinary medicine

Network meta-analysis (NMA) has become a popular method with which to conduct research syntheses pertaining to multiple interventions, as it allows for the comparison of interventions that have not been directly compared and typically improves precision of the comparative effect estimates relative to those produced via the individual trials. While the literature on NMA is well-developed, various problems have arisen in the process of conducting our own NMAs in the field of veterinary medicine that have not yet been addressed. Our research has focused on developing methods to fill these gaps. In Chapter 2, we propose an NMA model for an ordinal outcome that allows for outcome categorizations to vary across trials. It is common for the reporting of ordinal outcomes to differ from trial to trial, and the proposed model allows for data from all available trials to contribute to estimation of the comparative effects regardless of how the outcome was reported. In Chapter 3, we detail a procedure that can be used to design a connecting trial between two disconnected sub-networks such that the expected ranking of the best intervention is optimized. In Chapter 4, we propose using posterior model probabilities to select between component network meta-analysis models, a method that had not yet been implemented in the NMA literature. Our work therefore contributes to multiple facets of the NMA literature, including model formulation, utilization of the existing network to design a new trial, and model selection.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

dissertation

Discipline thesis:degree_discipline

Statistics

Department dc:contributor.department

Department of Statistics (LAS)

Grantor

Iowa State University

Year dc:date.issued

2023

Author dc:creator

• Morris, Paul S

Advisors dc:contributor.advisor

• Wang, Chong
• O'Connor, Annette M
• Ommen, Danica
• Zimmerman, Jeffrey
• Berg, Emily

Language dc:language.iso

en

DOI

https://doi.org/10.31274/td-20240617-204

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/RwyqQBkw