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  - GEN
A1  - Sieg, Tobias
A1  - Vogel, Kristin
A1  - Merz, Bruno
A1  - Kreibich, Heidi
T1  - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales
T2  - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
N2  - Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 743 
KW  - spatial scales
KW  - risk assessment
KW  - hydro-meterological hazards
KW  - object-based damage modeling
KW  - uncertainty
KW  - probabilistic approaches
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-435341
SN  - 1866-8372
IS  - 743
SP  - 574
EP  - 581
ER  -