Konietschke, Frank

[["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Journal of Statistical Software"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Noguchi, Kimihiro"],["dc.contributor.author","Gel, Yulia R."],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Konietschke, Frank"],["dc.date.accessioned","2019-07-10T08:14:06Z"],["dc.date.available","2019-07-10T08:14:06Z"],["dc.date.issued","2012-09"],["dc.description.abstract","Longitudinal data from factorial experiments frequently arise in various elds of study, ranging from medicine and biology to public policy and sociology. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semiparametric procedures that impose restrictive distributional assumptions on observed longitudinal samples becomes questionable. This, in turn, has led to a substantial demand for statistical procedures that enable us to accurately and reliably analyze longitudinal measurements in factorial experiments with minimal conditions on available data, and robust nonparametric methodology o ering such a possibility becomes of particular practical importance. In this article, we introduce a new R package nparLD which provides statisticians and researchers from other disciplines an easy and user-friendly access to the most up-todate robust rank-based methods for the analysis of longitudinal data in factorial settings. We illustrate the implemented procedures by case studies from dentistry, biology, and medicine."],["dc.format.extent","23"],["dc.identifier.fs","592126"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9492"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61433"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1548-7660"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","CC BY 3.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/3.0"],["dc.subject.ddc","610"],["dc.title","nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","73"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Strategies in Trauma and Limb Reconstruction"],["dc.bibliographiccitation.lastpage","79"],["dc.bibliographiccitation.volume","2009"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Stürmer, Klaus Michael"],["dc.contributor.author","Schütze, Gunther"],["dc.contributor.author","Frosch, K.-H."],["dc.contributor.author","Schwallich, T."],["dc.contributor.author","Losch, A."],["dc.contributor.author","Walde, T."],["dc.contributor.author","Balcarek, P."],["dc.date.accessioned","2019-07-09T11:52:50Z"],["dc.date.available","2019-07-09T11:52:50Z"],["dc.date.issued","2009"],["dc.description.abstract","Ligament graft fixation with bioabsorbable interference screws is a standard procedure in cruciate ligament replacement. Previous screw designs may resorb incompletely, and can cause osteolysis and sterile cysts despite being implanted for several years. The aim of this study was to examine the in vivo degradation and biocompatibility of the new Milagro™ interference screw (Mitek, Norderstedt, Germany). The Milagro™ interference screw is made of 30% ß-TCP (TriCalcium phosphate) and 70% PLGA (Poly-lactic-co-glycolic acid). In the period between June 2005 and February 2006, 38 patients underwent graft fixation with Milagro™ screws in our hospital. Arthroscopic ACL reconstruction was performed using hamstring tendon grafts in all the patients. MR imaging was performed on 12 randomly selected patients out of the total of 38 at 3, 6 and 12 months after surgery. During the examination, the volume loss of the screw, tunnel enlargement, presence of osteolysis, fluid lines, edema and postoperative screw replacement by bone tissue were evaluated. There was no edema or signs of inflammation around the bone tunnels. At 3, 6 and 12 months, the tibial screws showed an average volume loss of 0, 8.1% (±7.9%) and 82.6% (±17.2%, P < 0.05), respectively. The femoral screws showed volume losses of 2.5% (±2.1%), 31.3% (±21.6%) and 92.02% (±6.3%, P < 0.05), respectively. The femoral tunnel enlargement was 47.4% (±43.8%) of the original bone tunnel volume after 12 months, and the mean tunnel volume of the tibial tunnel was −9.5% (±58.1%) compared to the original tunnel. Bone ingrowth was observed in all the patients. In conclusion, the resorption behaviour of the Milagro™ screw is closely linked to the graft healing process. The screws were rapidly resorbed after 6 months and, at 12 months, only the screw remnants were detectable. Moreover, the Milagro™ screw is biocompatible and osteoconductive, promoting bone ingrowth during resorption. Tunnel enlargement is not prevented in the first months but is reduced by bone ingrowth after 12 months."],["dc.identifier.doi","10.1007/s11751-009-0063-2"],["dc.identifier.fs","434403"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6036"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60290"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1828-8936"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","Magnetic resonance imaging analysis of the bioabsorbable Milagro interference screw for graft fixation in anterior cruciate ligament reconstruction."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","616"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Biometrical Journal"],["dc.bibliographiccitation.lastpage","629"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Friede, Tim"],["dc.contributor.author","Pauly, Markus"],["dc.date.accessioned","2019-07-09T11:51:23Z"],["dc.date.available","2019-07-09T11:51:23Z"],["dc.date.issued","2019"],["dc.description.abstract","Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum-likelihood estimators. Although this approach can account for heterogeneity it postulates a common overdispersion parameter across groups. Such parametric assumptions are usually difficult to verify, especially in small trials. Therefore, novel procedures that are based on asymptotic results for newly developed rate and variance estimators are proposed in a general framework. Moreover, in case of small samples the procedures are carried out using permutation techniques. Here, the usual assumption of exchangeability under the null hypothesis is not met due to varying follow-up times and unequal overdispersion parameters. This problem is solved by the use of studentized permutations leading to valid inference methods for situations with (i) varying follow-up times, (ii) different overdispersion parameters, and (iii) small sample sizes."],["dc.identifier.doi","10.1002/bimj.201800027"],["dc.identifier.pmid","30515878"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59940"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/602144/EU//INSPIRE"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Semi‐parametric analysis of overdispersed count and metric data with varying follow‐up times: Asymptotic theory and small sample approximations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]