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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005712naa a2200445 4500
001oai:DiVA.org:uu-536095
003SwePub
008240815s2024 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5360952 URI
0247 a https://doi.org/10.1016/j.csda.2024.1080152 DOI
040 a (SwePub)uu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Nyberg, Joakim,d 1978-u Uppsala universitet,Institutionen för farmaci4 aut0 (Swepub:uu)jonyb109
2451 0a Optimizing designs in clinical trials with an application in treatment of Epidermolysis bullosa simplex, a rare genetic skin disease
264 1b Elsevier,c 2024
338 a electronic2 rdacarrier
520 a Epidermolysis bullosa simplex (EBS) skin disease is a rare disease, which renders the use of optimal design techniques especially important to maximize the potential information in a future study, that is, to make efficient use of the limited number of available subjects and observations. A generalized linear mixed effects model (GLMM), built on an EBS trial was used to optimize the design. The model assumed a full treatment effect in the follow-up period. In addition to this model, two models with either no assumed treatment effect or a linearly declining treatment effect in the follow-up were assumed. The information gain and loss when changing the number of EBS blisters counts, altering the duration of the treatment as well as changing the study period was assessed. In addition, optimization of the EBS blister assessment times was performed. The optimization was utilizing the derived Fisher information matrix for the GLMM with EBS blister counts and the information gain and loss was quantified by D-optimal efficiency. The optimization results indicated that using optimal assessment times increases the information of about 110120%, varying slightly between the assumed treatment models. In addition, the result showed that the assessment times were also sensitive to be moved +/- one week, but assessment times within +/- two days were not decreasing the information as long as three assessments (out of four assessments in the trial period) were within the treatment period and not in the follow-up period. Increasing the number of assessments to six or five per trial period increased the information to 130% and 115%, respectively, while decreasing the number of assessments to two or three, decreased the information to 50% and 80%, respectively. Increasing the length of the trial period had a minor impact on the information, while increasing the treatment period by two and four weeks had a larger impact, 120% and 130%, respectively. To conclude, general applications of optimal design methodology, derivation of the Fisher information matrix for GLMM with count data and examples on how optimal design could be used when designing trials for treatment of the EBS disease is presented. The methodology is also of interest for study designs where maximizing the information is essential. Therefore, a general applied research guidance for using optimal design is also provided.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Dermatologi och venereologi0 (SwePub)302042 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Dermatology and Venereal Diseases0 (SwePub)302042 hsv//eng
653 a Epidermolysis bullosa simplex
653 a Optimal design
653 a D-optimal
653 a Clinical trial design
7001 a Hooker, Andrew,d 1973-u Uppsala universitet,Institutionen för farmaci4 aut0 (Swepub:uu)ancho179
7001 a Zimmermann, Georgu Paracelsus Med Univ, Intelligent Data Analyt IDA Lab Salzburg, Team Biostat & Big Med Data, A-5020 Salzburg, Austria.;Paracelsus Med Univ Salzburg, Res & Innovat Management, A-5020 Salzburg, Austria.4 aut
7001 a Verbeeck, Johanu Paracelsus Med Univ Salzburg, Res & Innovat Management, A-5020 Salzburg, Austria.4 aut
7001 a Geroldinger, Martinu Paracelsus Med Univ, Intelligent Data Analyt IDA Lab Salzburg, Team Biostat & Big Med Data, A-5020 Salzburg, Austria.;Paracelsus Med Univ Salzburg, Res & Innovat Management, A-5020 Salzburg, Austria.4 aut
7001 a Thiel, Konstantin Emilu Paracelsus Med Univ, Intelligent Data Analyt IDA Lab Salzburg, Team Biostat & Big Med Data, A-5020 Salzburg, Austria.;Paracelsus Med Univ Salzburg, Res & Innovat Management, A-5020 Salzburg, Austria.4 aut
7001 a Molenberghs, Geertu Hasselt Univ, Data Sci Inst DSI, Interuniv Inst Biostat & Stat Bioinformat I BioSta, BE-3500 Hasselt, Belgium.;KULeuven, Interuniv Inst Biostat & Stat Bioinformat I BioSta, BE-3000 Leuven, Belgium.4 aut
7001 a Laimer, Martinu Paracelsus Med Univ Salzburg, Dept Dermatol & Allergol, Res Program Mol Therapy Genodermatoses, EB House Austria,Univ Hosp, A-5020 Salzburg, Austria.;Paracelsus Med Univ, Univ Hosp, Dept Dermatol & Allergol, A-5020 Salzburg, Austria.4 aut
7001 a Wally, Verenau Paracelsus Med Univ Salzburg, Dept Dermatol & Allergol, Res Program Mol Therapy Genodermatoses, EB House Austria,Univ Hosp, A-5020 Salzburg, Austria.4 aut
7102 a Uppsala universitetb Institutionen för farmaci4 org
7730 t Computational Statistics & Data Analysisd : Elsevierg 199q 199x 0167-9473x 1872-7352
8564 u https://doi.org/10.1016/j.csda.2024.108015y Fulltext
8564 u https://uu.diva-portal.org/smash/get/diva2:1889503/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-536095
8564 8u https://doi.org/10.1016/j.csda.2024.108015

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