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dc.contributor.authorFron Chabouis, Hélène
dc.contributor.authorChabouis, Francis
dc.contributor.authorGillaizeau, Florence
dc.contributor.authorDurieux, Pierre
dc.contributor.authorChatellier, Gilles
dc.contributor.authorRuse, N. Dorin
dc.contributor.authorAttal, Jean-Pierre
dc.date.accessioned2013-06-21T10:57:38Z
dc.date.available2013-06-21T10:57:38Z
dc.date.issued2014
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11455
dc.descriptionLe fichier attaché est le logiciel HERMES 1.0en
dc.language.isoenen
dc.subjectRandom allocationen
dc.subjectMinimizationen
dc.subjectStratified randomizationen
dc.subjectRandomized controlled trialsen
dc.subjectSimulationsen
dc.subjectPredictabilityen
dc.subject.ddc006en
dc.titleRandomization in clinical trials: stratification or minimization? The HERMES free simulation softwareen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenObjectives Operative clinical trials are often small and open-label. Randomization is therefore very important. Stratification and minimization are two randomization options in such trials. The first aim of this study was to compare stratification and minimization in terms of predictability and balance in order to help investigators choose the most appropriate allocation method. Our second aim was to evaluate the influence of various parameters on the performance of these techniques. Materials and methods The created software generated patients according to chosen trial parameters (e.g., number of important prognostic factors, number of operators or centers, etc.) and computed predictability and balance indicators for several stratification and minimization methods over a given number of simulations. Block size and proportion of random allocations could be chosen. A reference trial was chosen (50 patients, 1 prognostic factor, and 2 operators) and eight other trials derived from this reference trial were modeled. Predictability and balance indicators were calculated from 10,000 simulations per trial. Results Minimization performed better with complex trials (e.g., smaller sample size, increasing number of prognostic factors, and operators); stratification imbalance increased when the number of strata increased. An inverse correlation between imbalance and predictability was observed. Conclusions A compromise between predictability and imbalance still has to be found by the investigator but our software (HERMES) gives concrete reasons for choosing between stratification and minimization; it can be downloaded free of charge. Clinical relevance This software will help investigators choose the appropriate randomization method in future two-arm trials.en
dc.relation.isversionofjnlnameClinical Oral Investigations
dc.relation.isversionofjnlvol18
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2014
dc.relation.isversionofjnlpages25-34
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s00784-013-0949-8en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelMéthodes informatiques spécialesen
dc.relation.forthcomingnonen


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