{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:56:46Z","timestamp":1791464206098,"version":"4.3.4"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP23K11931"],"award-info":[{"award-number":["JP23K11931"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP22H03554"],"award-info":[{"award-number":["JP22H03554"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP24K21306"],"award-info":[{"award-number":["JP24K21306"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In meta-analyses of binary outcomes, two-stage methods based on the DerSimonian\u2013Laird-type random-effects model have been widely used, but these methods fix the within-study variances to their asymptotic estimates and rely on normality assumptions of the outcome measures. Such strict and often unrealistic assumptions can yield biased or inefficient inference. Likelihood-based approaches such as the binomial\u2013normal generalized linear mixed model and the hypergeometric-normal (HGN) model alleviate some of these issues but are intrinsically restricted to the odds-ratio scale, limiting interpretability and precluding direct estimation of the risk-ratio\u2014a more clinically meaningful measure. To overcome these limitations, we propose a unified likelihood-based framework that enables direct estimation of the risk-ratio under the HGN formulation. The proposed method introduces a pseudo\u2013data augmentation approach that preserves the conditional likelihood structure of the HGN model while yielding an unbiased estimating function for the log-risk-ratio. Higher-order inference is achieved via jackknife bias and variance corrections, ensuring accurate coverage even in small or sparse datasets. Simulation studies confirm nearly unbiased estimation and well-calibrated confidence intervals across a wide range of conditions. Applications to published meta-analyses demonstrate that this approach yields interpretable, coherent, and computationally tractable inference.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag169","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T19:26:55Z","timestamp":1789673215000},"source":"Crossref","is-referenced-by-count":0,"title":["Pseudo-data augmentation for exact conditional likelihood inference in meta-analysis"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2520-9949","authenticated-orcid":false,"given":"Hisashi","family":"Noma","sequence":"first","affiliation":[{"name":"Department of Interdisciplinary Statistical Mathematics, The Institute of Statistical Mathematics , Tachikawa, Tokyo 190-8562 ,","place":["Japan"]},{"name":"The Graduate Institute for Advanced Studies, The Graduate University for Advanced Studies (SOKENDAI) , Tachikawa, Tokyo 190-8562 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"2026100808120381200_bib1","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1136\/bmj.317.7168.1318","article-title":"Odds ratios should be avoided when events are common","volume":"317","author":"Altman","year":"1998","journal-title":"BMJ"},{"key":"2026100808120381200_bib2","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1002\/jrsm.1404","article-title":"Methods for estimating between-study variance and overall effect in meta-analysis of odds ratios","volume":"11","author":"Bakbergenuly","year":"2020","journal-title":"Research Synthesis Methods"},{"key":"2026100808120381200_bib3","first-page":"243","article-title":"Simulation studies of meta-analysis methods for binary outcomes: do they end with the same conclusions?","volume":"20","author":"Bakbergenuly","year":"2020","journal-title":"BMC Medical Research Methodology"},{"key":"2026100808120381200_bib4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12874-018-0531-9","article-title":"Meta-analysis of binary outcomes via generalized linear mixed models: A simulation study","volume":"18","author":"Bakbergenuly","year":"2018","journal-title":"BMC Medical Research Methodology"},{"key":"2026100808120381200_bib5","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00125-010-1992-5","article-title":"Rosiglitazone: A European regulatory perspective","volume":"54","author":"Blind","year":"2011","journal-title":"Diabetologia"},{"key":"2026100808120381200_bib6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1093\/biomet\/68.1.73","article-title":"Odds ratio estimators when the data are sparse","volume":"68","author":"Breslow","year":"1981","journal-title":"Biometrika"},{"key":"2026100808120381200_bib7","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1136\/bmj.316.7136.989","article-title":"When can odds ratios mislead?","volume":"316","author":"Davies","year":"1998","journal-title":"BMJ"},{"key":"2026100808120381200_bib8","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/0197-2456(86)90046-2","article-title":"Meta-analysis in clinical trials","volume":"7","author":"DerSimonian","year":"1986","journal-title":"Controlled Clinical Trials"},{"key":"2026100808120381200_bib9","volume-title":"The Jackknife, the Bootstrap, and Other Resampling Plans. 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