{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:55:08Z","timestamp":1774594508627,"version":"3.50.1"},"reference-count":23,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2017,2,6]],"date-time":"2017-02-06T00:00:00Z","timestamp":1486339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2017,2,6]],"date-time":"2017-02-06T00:00:00Z","timestamp":1486339200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"},{"start":{"date-parts":[[2017,2,6]],"date-time":"2017-02-06T00:00:00Z","timestamp":1486339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2017,4,30]]},"abstract":"<jats:p>Statistical inference for analyzing the results from several independent studies on the same quantity of interest has been investigated frequently in recent decades. Typically, any meta\u2010analytic inference requires that the quantity of interest is available from each study together with an estimate of its variability. The current work is motivated by a meta\u2010analysis on comparing two treatments (thoracoscopic and open) of congenital lung malformations in young children. Quantities of interest include continuous end\u2010points such as length of operation or number of chest tube days. As studies only report mean values (and no standard errors or confidence intervals), the question arises how meta\u2010analytic inference can be developed. We suggest two methods to estimate study\u2010specific variances in such a meta\u2010analysis, where only sample means and sample sizes are available in the treatment arms. A general likelihood ratio test is derived for testing equality of variances in two groups. By means of simulation studies, the bias and estimated standard error of the overall mean difference from both methodologies are evaluated and compared with two existing approaches: complete study analysis only and partial variance information. The performance of the test is evaluated in terms of type I error. Additionally, we illustrate these methods in the meta\u2010analysis on comparing thoracoscopic and open surgery for congenital lung malformations and in a meta\u2010analysis on the change in renal function after kidney donation. Copyright \u00a9 2017 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sim.7232","type":"journal-article","created":{"date-parts":[[2017,2,7]],"date-time":"2017-02-07T04:03:27Z","timestamp":1486440207000},"page":"1395-1413","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Statistical methodology for estimating the mean difference in a meta\u2010analysis without study\u2010specific variance information"],"prefix":"10.1002","volume":"36","author":[{"given":"Patarawan","family":"Sangnawakij","sequence":"first","affiliation":[{"name":"Department of Applied Statistics King Mongkut's University of Technology North Bangkok  Bangkok 10800 Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0638-7106","authenticated-orcid":false,"given":"Dankmar","family":"B\u00f6hning","sequence":"additional","affiliation":[{"name":"School of Mathematics &amp; Southampton 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