{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:41:08Z","timestamp":1792168868262,"version":"4.4.0"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2016,10,18]],"date-time":"2016-10-18T00:00:00Z","timestamp":1476748800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"EU's 7th Framework Programme for research, technological development, and demonstration","award":["FP HEALTH 2013602144"],"award-info":[{"award-number":["FP HEALTH 2013602144"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p>Random\u2010effects meta\u2010analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random\u2010effects meta\u2010analysis assumes approximately normal effect estimates and a normal random\u2010effects model. However, standard methods based on this model ignore the uncertainty in estimating the between\u2010trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung\u2010Knapp\u2010Sidik\u2010Jonkman method (HKSJ), the modified Knapp\u2010Hartung method (mKH, a variation of the HKSJ method) and Bayesian random\u2010effects meta\u2010analyses with priors covering plausible heterogeneity values; <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/bimj1725-math-0001.png\" xlink:title=\"urn:x-wiley:03233847:media:bimj1725:bimj1725-math-0001\"\/> code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes.<\/jats:p>","DOI":"10.1002\/bimj.201500236","type":"journal-article","created":{"date-parts":[[2016,10,18]],"date-time":"2016-10-18T14:39:15Z","timestamp":1476801555000},"page":"658-671","source":"Crossref","is-referenced-by-count":109,"title":["Meta\u2010analysis of two studies in the presence of heterogeneity with applications in rare diseases"],"prefix":"10.1002","volume":"59","author":[{"given":"Tim","family":"Friede","sequence":"first","affiliation":[{"name":"Department of Medical Statistics University Medical Center G\u00f6ttingen Humboldtallee 32 37073 G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"R\u00f6ver","sequence":"additional","affiliation":[{"name":"Department of Medical Statistics University Medical Center G\u00f6ttingen Humboldtallee 32 37073 G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Wandel","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG Oncology 4002 Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beat","family":"Neuenschwander","sequence":"additional","affiliation":[{"name":"Novartis Pharma AG Oncology 4002 Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2016,10,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199403033300901"},{"key":"e_1_2_8_3_1","unstructured":"Chen Y. 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