NETWORK META-ANALYSIS: A NOVEL APPROACH BASED ON A HIERARCHICAL DATA STRUCTURE
Creators: name: GRECO, TERESA
Publisher: name: Universit� degli Studi di Milano
Year: 2015

description: INTRODUCTION Meta-analysis is a powerful tool to cumulate and summarize the knowledge in a research field through statistical instruments, and to identify the overall measure of a treatment?s effect by combining several study-specific results. However, it is a controversial tool, because even small violations of certain rules can lead to misleading conclusions. Pooling data through meta-analysis can create problems, such as non linear correlations, multifactorial rather than unifactorial effects, limited coverage, or inhomogeneous data that fails to connect with the hypothesis. When head-to-head treatment comparisons are not available or conclusive, the limitations of standard (i.e. pairwise) meta-analyses can be overcome by network meta-analyses (NMA) which can provide estimates of treatment efficacy or safety of multiple treatment regimens. Different treatment strategies are analyzed by statistical inference methods rather than simply summing up trials that evaluated the same intervention compared to another intervention, standard care, or placebo. If a first trial compares drug A to drug B, showing that drug A is significantly superior to drug B, and a second trial investigates the same or a similar patient population comparing drug B versus drug C (demonstrating that drug B is equivalent to drug C), NMA may allow to infer that drug A is also potentially superior to drug C for this given patient population, even though there was no direct test of drug A against drug C. CONTENTS In this thesis we provided and discussed methods to overcome the limits of standard (univariate) meta-analysis, focusing on the ability to cope with multiple treatments and to deal with correlated data where correlation can derive from multiple endpoints, time-varying responses or from clustered observation. In the first chapter we explore the principal steps (from writing a prospective protocol of analysis to results? interpretation) in order to minimize the risk of conducting a mediocre meta-analysis and to support researchers to accurately evaluate the published findings. The second chapter represents an overview of conceptual and practical issues of a network meta-analysis. We start from general considerations on network meta-analysis to specifically appraise how to collect study data, structure the analytical network, and specify the requirements for different models and parameter interpretations. Specifically, we outline the key steps, from literature search to sensitivity analysis, necessary to perform a valid network meta-analysis on binomial data. In the third party of this work, we focus our attention on data which can be analyzed with a binomial model applying the Bayesian hierarchical approach and using Markov Chain Monte Carlo approach. We also apply this analytical approach to a case study on the beneficial effects of anesthetic agents in order to further clarify the statistical details of the models, diagnostics, and computations. We presented a practical guide with the actual WinBUGS and SAS codes to allow transparency and ease of replication of all steps that are required when carrying out such quantitative syntheses. In the fourth chapter we propose an alternative frequentist approach to estimate consistency and inconsistency models for a network meta-analysis. We discuss the multilevel network meta-analysis which include a three-level data structure: subjects within studies at the first level, studies within study designs at the second level and design configuration at the third level. We discuss multilevel modeling which may be carried out within widely available statistical programs such as SAS software, and we compare the results of a published Bayesian network meta-analysis on a binary endpoint which examines the effect on mortality of desflurane, isoflurane, sevoflurane, and total intravenous anaesthetics at the longest follow-up available. In the final chapter we compare the Bayesian and the novel frequentist-multilevel approach in performing network meta-analysis on publicly available data and we investigate the descriptive characteristics that may contribute to decrease or increase the potential difference between the estimates derived from the two approaches. The two approaches were compared in terms of the difference between the pooled estimates or their standardized values, and of the Euclidean distance. BAYESIAN NETWORK META-ANALYSIS Suppose that J trials provide mixed comparisons among K treatments and that a is the trial-specific reference treatment. The random effect model is defined by: yja= ?0+eja for j=1,2,...,J; a=1,2,?,K-1 yjk= ?0j+?j,ak+ejk for j=1,2,...,J; a=2,3,?,K; b