Konietschke, Frank

[["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.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"]]