title: Higher-order likelihood inference in meta-analysis and meta-regression.
abstract: This paper investigates the use of likelihood methods for meta-analysis, within the random-effects models framework. We show that likelihood inference relying on first-order approximations, while improving common meta-analysis techniques, can be prone to misleading results. This drawback is very evident in the case of small sample sizes, which are typical in meta-analysis. We alleviate the problem by exploiting the theory of higher-order asymptotics. In particular, we focus on a second-order adjustment to the log-likelihood ratio statistic. Simulation studies in meta-analysis and meta-regression show that higher-order likelihood inference provides much more accurate results than its first-order counterpart, while being of a computationally feasible form. We illustrate the application of the proposed approach on a real example.
authors.affiliation: University of Verona, via dell'Artigliere 19, I-37129, Italy. annamaria.guolo@univr.it
authors.forename: Annamaria
authors.initials: A
authors.lastname: Guolo
mesh_terms: D001500:BCG Vaccine / Q000627:therapeutic use; D003198:Computer Simulation / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D012044:Regression Analysis; D018401:Sample Size; D014376:Tuberculosis / Q000517:prevention & control
chemical_list: D001500:BCG Vaccine