Konietschke, Frank

[["dc.bibliographiccitation.firstpage","63"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","The International Journal of Biostatistics"],["dc.bibliographiccitation.lastpage","73"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Boesiger, Sandra"],["dc.contributor.author","Brunner, Edgar"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:24:47Z"],["dc.date.available","2018-11-07T09:24:47Z"],["dc.date.issued","2013"],["dc.description.abstract","Multiple contrast tests can be used to test arbitrary linear hypotheses by providing local and global test decisions as well as simultaneous confidence intervals. The ANOVA-F-test on the contrary can be used to test the global null hypothesis of no treatment effect. Thus, multiple contrast tests provide more information than the analysis of variance (ANOVA) by offering which levels cause the significance. We compare the exact powers of the ANOVA-F-test and multiple contrast tests to reject the global null hypothesis. Hereby, we compute their least favorable configurations (LFCs). It turns out that both procedures have the same LFCs under certain conditions. Exact power investigations show that their powers are equal to detect their LFCs."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, Ho 1687/9-1]"],["dc.identifier.doi","10.1515/ijb-2012-0020"],["dc.identifier.isi","000329433300005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29910"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Walter De Gruyter Gmbh"],["dc.relation.issn","1557-4679"],["dc.relation.issn","2194-573X"],["dc.title","Are Multiple Contrast Tests Superior to the ANOVA?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e31242"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Libiger, Ondrej"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:13:21Z"],["dc.date.available","2018-11-07T09:13:21Z"],["dc.date.issued","2012"],["dc.description.abstract","Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test."],["dc.description.sponsorship","DFG [Br 655/16-1, HO-1687/9]"],["dc.identifier.doi","10.1371/journal.pone.0031242"],["dc.identifier.isi","000302873700038"],["dc.identifier.pmid","22363593"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7880"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27154"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Nonparametric Evaluation of Quantitative Traits in Population-Based Association Studies when the Genetic Model is Unknown"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","738"],["dc.bibliographiccitation.journal","Electronic Journal of Statistics"],["dc.bibliographiccitation.lastpage","759"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:15:04Z"],["dc.date.available","2018-11-07T09:15:04Z"],["dc.date.issued","2012"],["dc.description.abstract","We study simultaneous rank procedures for unbalanced designs with independent observations. The hypotheses are formulated in terms of purely nonparametric treatment effects. In this context, we derive rank-based multiple contrast test procedures and simultaneous confidence intervals which take the correlation between the test statistics into account. Hereby, the individual test decisions and the simultaneous confidence intervals are compatible. This means, whenever an individual hypothesis has been rejected by the multiple contrast test, the corresponding simultaneous confidence interval does not include the null, i.e. the hypothetical value of no treatment effect. The procedures allow for testing arbitrary purely nonparametric multiple linear hypotheses(e.g. many-to-one, all-pairs, changepoint, or even average comparisons). We do not assume homogeneous variances of the data; in particular, the distributions can have different shapes even under the null hypothesis. Thus, a solution to the multiple nonparametric Behrens-Fisher problem is presented in this unified framework."],["dc.description.sponsorship","German Research Foundation [DFG-Br 655/16-1, HO 1687/9-1]"],["dc.identifier.doi","10.1214/12-EJS691"],["dc.identifier.isi","000306915200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9504"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27587"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Inst Mathematical Statistics"],["dc.relation.issn","1935-7524"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Rank-based multiple test procedures and simultaneous confidence intervals"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1895"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Computational Statistics & Data Analysis"],["dc.bibliographiccitation.lastpage","1905"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Bathke, Arne C."],["dc.contributor.author","Hothorn, Ludwig A."],["dc.contributor.author","Brunner, E."],["dc.date.accessioned","2018-11-07T08:40:41Z"],["dc.date.available","2018-11-07T08:40:41Z"],["dc.date.issued","2010"],["dc.description.abstract","The several sample case of the so-called nonparametric Behrens-Fisher problem in repeated measures designs is considered. That is, even under the null hypothesis, the marginal distribution functions in the different groups may have different shapes, and are not assumed to be equal. Moreover, the continuity of the marginal distribution functions is not required so that data with ties and, particularly, ordered categorical data are covered by this model. A multiple relative treatment effect is defined which can be estimated by using the mid-ranks of the observations within pairwise samples. The asymptotic distribution of this estimator is derived, along with a consistent estimator of its asymptotic covariance matrix. In addition, a multiple contrast test and related simultaneous confidence intervals for the relative marginal effects are derived and compared to rank-based Wald-type and ANOVA-type statistics. Simulations show that the ANOVA-type statistic and the multiple contrast test appear to maintain the pre-assigned level of the test quite accurately (even for rather small sample sizes) while the Wald-type statistic leads, as expected, to somewhat liberal decisions. Regarding the power, none of the statistics is uniformly superior. A real data set illustrates the application. (C) 2010 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.csda.2010.02.019"],["dc.identifier.isi","000278155100001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19290"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","0167-9473"],["dc.title","Testing and estimation of purely nonparametric effects in repeated measures designs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","14"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Statistics in Biopharmaceutical Research"],["dc.bibliographiccitation.lastpage","27"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2018-11-07T09:15:03Z"],["dc.date.available","2018-11-07T09:15:03Z"],["dc.date.issued","2012"],["dc.description.abstract","The U.S. National Toxicology Program uses Shirley's test for the evaluation of endpoints in hematology, clinical chemistry, and urinalysis, which are assumed to be skewed distributed. Until now, no algorithm for Shirley's test for general unbalanced designs has been devised. A Shirley-type procedure is provided for estimating multiplicity-adjusted p-values or simultaneous confidence intervals for any continuous or discrete distributions and possible heterogeneous variances. Routine evaluation can be performed using the open-source R package nparcomp. The use of simultaneous confidence intervals for the relative effect is recommended, since these scale-invariant intervals allow one to claim the statistical significance of multiple endpoints as well as their biological relevance."],["dc.description.sponsorship","German Science Foundation [Br-655/16-1, HO 1687/9-1]"],["dc.identifier.doi","10.1080/19466315.2011.633861"],["dc.identifier.isi","000307662900002"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27581"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Statistical Assoc"],["dc.relation.issn","1946-6315"],["dc.title","Evaluation of Toxicological Studies Using a Nonparametric Shirley-Type Trend Test for Comparing Several Dose Levels with a Control Group"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Journal of Statistical Software"],["dc.bibliographiccitation.volume","64"],["dc.contributor.author","Konietschke, Frank"],["dc.contributor.author","Placzek, Marius"],["dc.contributor.author","Schaarschmidt, Frank"],["dc.contributor.author","Hothorn, Ludwig A."],["dc.date.accessioned","2019-07-09T11:42:35Z"],["dc.date.available","2019-07-09T11:42:35Z"],["dc.date.issued","2015"],["dc.description.abstract","One-way layouts, i.e., a single factor with several levels and multiple observations at each level, frequently arise in various elds. Usually not only a global hypothesis is of interest but also multiple comparisons between the di erent treatment levels. 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 samples becomes questionable. This, in turn, emphasizes the demand on statistical procedures that enable us to accurately and reliably analyze one-way layouts with minimal conditions on available data. Nonparametric methods o er such a possibility and thus become of particular practical importance. In this article, we introduce a new R package nparcomp which provides an easy and user-friendly access to rank-based methods for the analysis of unbalanced one-way layouts. It provides procedures performing multiple comparisons and computing simultaneous con dence intervals for the estimated e ects which can be easily visualized. The special case of two samples, the nonparametric Behrens-Fisher problem, is included. We illustrate the implemented procedures by examples from biology and medicine."],["dc.identifier.doi","10.18637/jss.v064.i09"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13580"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58699"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1548-7660"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","nparcomp : An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]