Bayesian inference for network meta-regression using multivariate random effects with applications to cholesterol lowering drugs.

Autor:	Li H; Department of Statistics, University of Connecticut, Storrs, CT, USA., Chen MH; Department of Statistics, University of Connecticut, Storrs, CT, USA., Ibrahim JG; Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA., Kim S; Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA., Shah AK; Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA., Lin J; Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA., Tershakovec AM; Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA.
Jazyk:	angličtina
Zdroj:	Biostatistics (Oxford, England) [Biostatistics] 2019 Jul 01; Vol. 20 (3), pp. 499-516.
DOI:	10.1093/biostatistics/kxy014
Abstrakt:	Low-density lipoprotein cholesterol (LDL-C) has been identified as a causative factor for atherosclerosis and related coronary heart disease, and as the main target for cholesterol- and lipid-lowering therapy. Statin drugs inhibit cholesterol synthesis in the liver and are typically the first line of therapy to lower elevated levels of LDL-C. On the other hand, a different drug, Ezetimibe, inhibits the absorption of cholesterol by the small intestine and provides a different mechanism of action. Many clinical trials have been carried out on safety and efficacy evaluation of cholesterol lowering drugs. To synthesize the results from different clinical trials, we examine treatment level (aggregate) network meta-data from 29 double-blind, randomized, active, or placebo-controlled statins +/$-$ Ezetimibe clinical trials on adult treatment-naïve patients with primary hypercholesterolemia. In this article, we propose a new approach to carry out Bayesian inference for arm-based network meta-regression. Specifically, we develop a new strategy of grouping the variances of random effects, in which we first formulate possible sets of the groups of the treatments based on their clinical mechanisms of action and then use Bayesian model comparison criteria to select the best set of groups. The proposed approach is especially useful when some treatment arms are involved in only a single trial. In addition, a Markov chain Monte Carlo sampling algorithm is developed to carry out the posterior computations. In particular, the correlation matrix is generated from its full conditional distribution via partial correlations. The proposed methodology is further applied to analyze the network meta-data from 29 trials with 11 treatment arms. (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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