{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:09:03Z","timestamp":1790150943306,"version":"4.0.1"},"reference-count":24,"publisher":"Wiley","issue":"20-22","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100017569","name":"Salzburger Landesregierung","doi-asserted-by":"publisher","award":["20102\/F2300645\u2010FPR"],"award-info":[{"award-number":["20102\/F2300645\u2010FPR"]}],"id":[{"id":"10.13039\/501100017569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020098","name":"Innovative Health Initiative","doi-asserted-by":"publisher","award":["101165912"],"award-info":[{"award-number":["101165912"]}],"id":[{"id":"10.13039\/100020098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Multivariate endpoints are increasingly used in clinical studies on rare diseases, where sample sizes tend to be small and distributional assumptions are often violated. In such settings, commonly applied parametric multivariate methods, such as classical\n                    <jats:styled-content>MANOVA<\/jats:styled-content>\n                    approaches, may suffer from inflated type\u2010I error or reduced statistical power. Therefore, a neutral simulation\u2010based comparison of four methodologically different approaches for the analysis of multivariate outcomes in parallel group designs was conducted. The four approaches included the nonparametric R package\n                    <jats:styled-content>npmv<\/jats:styled-content>\n                    (using permutation and randomization resampling for small samples), a semiparametric\n                    <jats:italic>MANOVA<\/jats:italic>\n                    approach from the R package\n                    <jats:styled-content>MANOVA.RM<\/jats:styled-content>\n                    with both a Wald\u2010type statistics (WTS) and a modified ANOVA\u2010type statistics (MATS) using all available variants including also bootstrap approaches, a classical parametric\n                    <jats:italic>MANOVA<\/jats:italic>\n                    , and multiple univariate Mann\u2013Whitney tests with Holm correction. The simulation framework was informed by empirical distributions derived from anonymized clinical data and clinical expert guidance. Two clinically realistic effect types were mainly evaluated: (i) percent\u2010responder effects for wound size reduction and (ii) additional location shifts in pain and pruritus visual analog scales (VAS) scores. In addition, scenarios are examined in which classical assumptions such as multivariate normality and homogeneity of covariance matrices are violated. The sample sizes considered ranged from 12 to 360 subjects. Both balanced and unbalanced designs were evaluated, with the objective of illustrating potential limitations of methods when applied to small samples. Outcomes were assessed in terms of type\u2010I error control and statistical power. Overall,\n                    <jats:styled-content>npmv<\/jats:styled-content>\n                    , the MATS using especially parametric bootstrap within the semiparametric\n                    <jats:styled-content>MANOVA.RM<\/jats:styled-content>\n                    package maintained reliable type\u2010I error control, whereas the semiparametric\n                    <jats:styled-content>MANOVA.RM<\/jats:styled-content>\n                    approach with the WTS using its asymptotic approximation showed very liberal type\u2010I error behavior to almost no type\u2010I error control in small sample scenarios, and especially in unbalanced settings. Bootstrap approaches especially of the WTS substantially improved type\u2010I error control compared with the asymptotic WTS. The nonparametric\n                    <jats:styled-content>npmv<\/jats:styled-content>\n                    approach achieved good power values, especially in small samples, as did the modified ATS approach using parametric bootstrap from the\n                    <jats:styled-content>MANOVA.RM<\/jats:styled-content>\n                    package, which achieved very similar results. The univariate Mann\u2013Whitney U test with Holm correction tended to yield conservative results for small samples; however, despite the univariate nature of this approach, it achieved a high and comparable level of statistical power in many scenarios. Classical MANOVA also yielded good results in certain scenarios; however, it exhibited rather liberal type\u2010I error rates, primarily in heteroscedastic scenarios and unbalanced designs, which can be explained by a violation of the assumption of homoscedastic covariance matrices. These results highlight the strengths and limitations of a multi\u2010aspect analysis in parallel group designs especially for small sample settings, as common in rare diseases, and provide guidance for the analysis in rare disease research.\n                  <\/jats:p>","DOI":"10.1002\/sim.70735","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T03:16:10Z","timestamp":1790046970000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Neutral Comparison of Multivariate Hypothesis Tests for Parallel Group Designs in Rare Disease Settings"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7858-323X","authenticated-orcid":false,"given":"Martin","family":"Geroldinger","sequence":"first","affiliation":[{"name":"Research Program Biomedical Data Science Paracelsus Medical University  Salzburg Austria"},{"name":"Team Biostatistics and Big Medical Data, IDA Lab Salzburg Paracelsus Medical University  Salzburg Austria"},{"name":"Department of Neurology, Christian Doppler Medical Centre, Full Member of European Reference Network on Rare and Complex Epilepsies EpiCARE Paracelsus Medical University  Salzburg Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Laimer","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergology Paracelsus Medical University  Salzburg Austria"},{"name":"EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology University Hospital of the Paracelsus Medical University Salzburg  Salzburg Austria"},{"name":"EB Research Institute GmbH  Salzburg Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8705-3890","authenticated-orcid":false,"given":"Verena","family":"Wally","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergology Paracelsus Medical University  Salzburg Austria"},{"name":"EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology University Hospital of the Paracelsus Medical University Salzburg  Salzburg Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6085-9170","authenticated-orcid":false,"given":"Johann W.","family":"Bauer","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergology Paracelsus Medical University  Salzburg Austria"},{"name":"EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology University Hospital of the Paracelsus Medical University Salzburg  Salzburg Austria"},{"name":"EB Research Institute GmbH  Salzburg Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8282-1034","authenticated-orcid":false,"given":"Georg","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"Research Program Biomedical Data Science Paracelsus Medical University  Salzburg Austria"},{"name":"Team Biostatistics and Big Medical Data, IDA Lab Salzburg Paracelsus Medical University  Salzburg Austria"},{"name":"Department of Artificial Intelligence and Human Interfaces University of Salzburg  Salzburg Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.4135\/9781412985222"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v076.i04"},{"issue":"2","key":"e_1_2_11_4_1","first-page":"380","article-title":"Resampling\u2010Based Analysis of Multivariate Data and Repeated Measures Designs 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