Academic Journal
Bootstrap Calibration of Likelihood-Ratio Inference for the Hypergeometric-Normal Random-Effects Model in Rare-Event Meta-Analysis.
| Τίτλος: | Bootstrap Calibration of Likelihood-Ratio Inference for the Hypergeometric-Normal Random-Effects Model in Rare-Event Meta-Analysis. |
|---|---|
| Συγγραφείς: | Noma H; Department of Interdisciplinary Statistical Mathematics, The Institute of Statistical Mathematics, Tokyo, Japan.; The Graduate Institute for Advanced Studies, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan., Evrenoglou T; Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany., Maruo K; Department of Biostatistics, Institute of Medicine, University of Tsukuba, Tsukuba, Japan. |
| Πηγή: | Statistics in medicine [Stat Med] 2026 Oct; Vol. 45 (23-24), pp. e70776. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Ιατρικοί όροι (MeSH): | Meta-Analysis as Topic* , Models, Statistical*, Thiazolidinediones/therapeutic use ; Thiazolidinediones/adverse effects ; Likelihood Functions ; Humans ; Computer Simulation ; Rosiglitazone ; Confidence Intervals ; Data Interpretation, Statistical |
| Περίληψη: | Conventional random-effects models in meta-analysis rely on normal approximations and fixed within-study variance assumptions that are often violated in rare-event settings. When zero cells occur, Haldane-type (+0.5) corrections can further bias the estimated effects. To overcome these limitations, likelihood-based approaches such as the hypergeometric-normal (HGN) model have been developed to enable exact conditional likelihood-based inference. However, ordinary Wald-type inference may still be inaccurate in small and sparse data settings. In this article, we investigate likelihood-ratio (LR)-based inference for the HGN random-effects model and propose two parametric bootstrap procedures for improving its finite-sample calibration: (1) bootstrap mean calibration of the LR statistic and (2) bootstrap inference based directly on the empirical distribution of the LR statistic. Extensive simulation studies under rare-event conditions demonstrate that the uncorrected LR-based confidence interval already provides stable coverage, reflecting the favorable properties of the exact conditional likelihood. Bootstrap mean calibration generally provided the most consistent additional calibration under the most challenging extremely rare-event settings, whereas direct bootstrap inference provided an alternative distribution-based approach. An application to a meta-analysis of rosiglitazone and myocardial infarction is also presented. (© 2026 John Wiley & Sons Ltd.) |
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| Grant Information: | JP23K11931 Japan Society for the Promotion of Science; JP22H03554 Japan Society for the Promotion of Science; JP24K21306 Japan Society for the Promotion of Science; JP26K02873 Japan Society for the Promotion of Science; 554095932 Deutsche Forschungsgemeinschaft |
| Contributed Indexing: | Keywords: bootstrap; exact conditional likelihood; likelihood‐ratio inference; meta‐analysis; rare events |
| Substance Nomenclature: | 0 (Thiazolidinediones) 05V02F2KDG (Rosiglitazone) |
| Entry Date(s): | Date Created: 20261013 Date Completed: 20261013 Latest Revision: 20261014 |
| Update Code: | 20261015 |
| DOI: | 10.1002/sim.70776 |
| PMID: | 42869622 |
| Βάση Δεδομένων: | MEDLINE |
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