Date of Completion
8-10-2020
Embargo Period
8-9-2025
Keywords
Network Meta Analysis, Likelihood Ratio Test, Plausibility Index, Hypothesis Testing, Bucher's Test, Inconsistency Dectection
Major Advisor
Ming-Hui Chen
Associate Advisor
Lynn Kuo
Associate Advisor
Dipak Dey
Field of Study
Statistics
Degree
Doctor of Philosophy
Open Access
Open Access
Abstract
One of the long-standing methodological issues in network meta analysis (NMA) is that of assessing homogeneity and consistency in treatment comparisons. In this dissertation, we construct general linear hypotheses to investigate homogeneity and consistency under general fixed effects models, one-equation and two-equation models, within Frequentist and Bayesian framework, respectively. We introduce the concept of 'inconsistency testable loops', which is the fundamental key to inconsistency detection in NMA. An algorithm is developed to compute all the inconsistency testable loops in network meta-data, as well as the contrast matrices under homogeneity and consistency assumptions. Under the normal fixed effects model, we show the equivalence of the likelihood ratio test (LRT) under the proposed linear hypotheses and Bucher's method for testing inconsistency based on comparison of the weighted averages of direct and indirect treatment effects. A novel Plausibility Index (PI) is developed to assess homogeneity and consistency. Theoretical properties of the proposed methodology are examined in details. A road map of treatment comparisons %while adjusting for heterogeneity and inconsistency is given. We apply the proposed methodology to analyze the network meta data from 29 randomized clinical trials with 11 treatment arms on safety and efficacy evaluation of cholesterol lowering drugs.
Recommended Citation
Zhang, Cheng, "Heterogeneity and Inconsistency Detection in Network Meta-Analysis with Applications to Cholesterol-Lowering Drugs" (2020). Doctoral Dissertations. 2630.
https://digitalcommons.lib.uconn.edu/dissertations/2630