TY - JOUR A1 - Mühlenbruch, Kristin A1 - Kuxhaus, Olga A1 - Pencina, Michael J. A1 - Boeing, Heiner A1 - Liero, Hannelore A1 - Schulze, Matthias Bernd T1 - A confidence ellipse for the Net Reclassification Improvement JF - European journal of epidemiology N2 - The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study. KW - Risk assessment KW - Risk model KW - Model comparison KW - Reclassification KW - Confidence intervals Y1 - 2015 U6 - https://doi.org/10.1007/s10654-015-0001-1 SN - 0393-2990 SN - 1573-7284 VL - 30 IS - 4 SP - 299 EP - 304 PB - Springer CY - Dordrecht ER - TY - GEN A1 - Mühlenbruch, Kristin A1 - Kuxhaus, Olga A1 - Pencina, Michael J. A1 - Boeing, Heiner A1 - Liero, Hannelore A1 - Schulze, Matthias Bernd T1 - A confidence ellipse for the Net Reclassification Improvement T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study. KW - risk assessment KW - risk model KW - model comparison KW - reclassification KW - confidence intervals Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427371 SN - 1866-8372 IS - 825 SP - 299 EP - 304 ER - TY - INPR A1 - Liero, Hannelore T1 - A Note on : testing the Copula Based on Densities N2 - We consider the problem of testing whether the density of a mul- tivariate random variable can be expressed by a prespecified copula function and the marginal densities. The proposed test procedure is based on the asymptotic normality of the properly standardized integrated squared distance between a multivariate kernel density estimator and an estimator of its expectation under the hypothesis. The test of independence is a special case of this approach. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2006, 02 Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-49393 ER - TY - BOOK A1 - Konakov, Valentin S. A1 - Läuter, Henning A1 - Liero, Hannelore T1 - Comparison of the asymptotic power of tests based on L2- and L-norms under non-standard local alternatives T3 - Discussion Paper / Humboldt-Universität zu Berlin, Institut für Mathematik, SFB 373 Y1 - 1995 VL - 10 CY - Berlin ER - TY - JOUR A1 - Wichitsa-Nguan, Korakot A1 - Läuter, Henning A1 - Liero, Hannelore T1 - Estimability in Cox models JF - Statistical Papers N2 - Our procedure of estimating is the maximum partial likelihood estimate (MPLE) which is the appropriate estimate in the Cox model with a general censoring distribution, covariates and an unknown baseline hazard rate . We find conditions for estimability and asymptotic estimability. The asymptotic variance matrix of the MPLE is represented and properties are discussed. KW - Cox model KW - Estimability KW - Asymptotic variance of maximum partial likelihood estimate Y1 - 2016 U6 - https://doi.org/10.1007/s00362-016-0755-x SN - 0932-5026 SN - 1613-9798 VL - 57 SP - 1121 EP - 1140 PB - Springer CY - New York ER - TY - BOOK A1 - Liero, Hannelore T1 - Estimation and testing the effect of covariates in accelerated life time models under censoring N2 - The accelerated lifetime model is considered. To test the influence of the covariate we transform the model in a regression model. Since censoring is allowed this approach leads to a goodness-of-fit problem for regression functions under censoring. So nonparametric estimation of regression functions under censoring is investigated, a limit theorem for a L2-distance is stated and a test procedure is formulated. Finally a Monte Carlo procedure is proposed. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2010, 02 KW - accelerated life time model KW - censoring KW - goodness-of-fit testing KW - nonparametric regression estimation KW - Monte Carlo testing Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-52823 ER - TY - BOOK A1 - Liero, Hannelore T1 - Estimation and testing the effect of covariates in accelerated life time models under censoring T3 - Preprint / Universität Potsdam, Institut für Mathematik, Mathematische Statistik un Y1 - 2010 SN - 1613-3307 PB - Univ. CY - Potsdam ER - TY - THES A1 - Liero, Hannelore T1 - Global measures of deviation of nonparametric curve estimators : strong uniform consistency and limit theorems Y1 - 1999 CY - Potsdam ER - TY - BOOK A1 - Liero, Hannelore T1 - Goodness of fit tests of L2-Type T3 - Preprint / Universität Potsdam, Institut für Mathematik, Arbeitsgruppe Partiell Y1 - 2003 SN - 1437-739X PB - Univ. CY - Potsdam ER - TY - INPR A1 - Liero, Hannelore T1 - Goodness of Fit Tests of L2-Type N2 - We give a survey on procedures for testing functions which are based on quadratic deviation measures. The following problems are considered: Testing whether a density function lies in a parametric class of functions, whether continuous random variables are independent; testing cell probabilities and independence in sparse data sets; testing the parametric fit of a regression homoscedasticity in a regression model and testing the hazard rate in survival models with censoring and with and without covariates. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2003, 15 Y1 - 2003 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-51494 ER -