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http://dx.doi.org/10.25673/43006
Titel: | Optimal design in hierarchical random effect models for individual prediction with application in precision medicine |
Autor(en): | Prus, Maryna Benda, Norbert Schwabe, Rainer |
Erscheinungsdatum: | 2020 |
Art: | Artikel |
Sprache: | Englisch |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-449604 |
Schlagwörter: | Hierarchical random effect models Clinical research Optimal designs Individual predictions |
Zusammenfassung: | Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population parameters related to the expected treatment effects or group differences among all units of an upper level (e.g. subjects in many settings). Optimal design for estimation of population parameters are well established for many models. However, optimal designs for the prediction for the individual units may be different. Several settings are identified in which individual prediction may be of interest. In this paper, we determine optimal designs for the individual predictions, e.g. in multi-cluster trials or in trials that investigate a new treatment in a number of different subpopulations, and compare them to a conventional balanced design with respect to treatment allocation. Our investigations show that in the case of uncorrelated cluster intercepts and cluster treatments the optimal allocations are far from being balanced if the treatment effects vary strongly as compared to the residual error and more subjects should be recruited to the active (new) treatment. Nevertheless, efficiency loss may be limited resulting in a moderate sample size increase when individual predictions are foreseen with a balanced allocation. |
URI: | https://opendata.uni-halle.de//handle/1981185920/44960 http://dx.doi.org/10.25673/43006 |
Open-Access: | ![]() |
Nutzungslizenz: | ![]() |
Sponsor/Geldgeber: | Projekt DEAL 2020 |
Journal Titel: | Journal of statistical theory and practice |
Verlag: | Springer International Publishing |
Verlagsort: | Cham |
Band: | 14 |
Heft: | 2 |
Originalveröffentlichung: | 10.1007/s42519-020-00090-y |
Seitenanfang: | 1 |
Seitenende: | 12 |
Enthalten in den Sammlungen: | Fakultät für Mathematik (OA) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Prus et al._Optimal design_2020.pdf | Zweitveröffentlichung | 876.21 kB | Adobe PDF | ![]() Öffnen/Anzeigen |