title: Likelihood ratio meta-analysis: New motivation and approach for an old method.
abstract: A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience.
authors.affiliation: Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: colin.dormuth@ti.ubc.ca.
authors.forename: Colin R
authors.initials: CR
authors.lastname: Dormuth
authors.affiliation: Center for Clinical Epidemiology, Lady Davis Research Institute, Jewish General Hospital, Montreal, Quebec, Canada; Department of Medicine, McGill University, Montreal, Quebec, Canada.
authors.forename: Kristian B
authors.initials: KB
authors.lastname: Filion
authors.affiliation: Departments of Pediatrics and of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Platt
mesh_terms: D001241:Aspirin / Q000627:therapeutic use; D001499:Bayes Theorem; D000077144:Clopidogrel; D003627:Data Interpretation, Statistical; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000517:prevention & control; D010975:Platelet Aggregation Inhibitors / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D020521:Stroke / Q000517:prevention & control; D013988:Ticlopidine / Q000031:analogs & derivatives / Q000627:therapeutic use; D016896:Treatment Outcome
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D010975:Platelet Aggregation Inhibitors; D000077144:Clopidogrel; D013988:Ticlopidine; D001241:Aspirin
keywords: Likelihood ratio; Meta-analysis; Meta-analytic methods
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