title: Meta-analysis of few small studies in orphan diseases.
abstract: Meta-analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random-effects meta-analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between-trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp-Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between-trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half-normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
authors.affiliation: Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Friede
authors.affiliation: Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors.forename: Christian
authors.identifier: http://orcid.org/0000-0002-6911-698X
authors.initials: C
authors.lastname: Röver
authors.affiliation: Novartis Pharma AG, Basel, Switzerland.
authors.forename: Simon
authors.initials: S
authors.lastname: Wandel
authors.affiliation: Novartis Pharma AG, Basel, Switzerland.
authors.forename: Beat
authors.initials: B
authors.lastname: Neuenschwander
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006084:Graft Rejection; D006801:Humans; D007166:Immunosuppressive Agents / Q000627:therapeutic use*; D017093:Liver Failure / Q000601:surgery*; D016031:Liver Transplantation; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D010372:Pediatrics; D011381:Programming Languages; D035583:Rare Diseases / Q000628:therapy*; D015203:Reproducibility of Results; D012107:Research Design; D012196:Review Literature as Topic; D018401:Sample Size; D012984:Software
chemical_list: D007166:Immunosuppressive Agents
keywords: Bayesian statistics; between-study heterogeneity; coverage probability; meta-analysis; small population