@article{MuehlenbruchZhuoBardenheieretal.2019,
  author    = {M{\"u}hlenbruch, Kristin and Zhuo, Xiaohui and Bardenheier, Barbara and Shao, Hui and Laxy, Michael and Icks, Andrea and Zhang, Ping and Gregg, Edward W. and Schulze, Matthias Bernd},
  title     = {Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention},
  series = {Acta Diabetologica},
  volume    = {57},
  journal   = {Acta Diabetologica},
  number    = {4},
  publisher = {Springer},
  address   = {Mailand},
  issn      = {0001-5563},
  doi       = {10.1007/s00592-019-01451-1},
  pages     = {447 -- 454},
  year      = {2019},
  abstract  = {Aims: Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. Methods: We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). Results: In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < \$50,000/QALY gained in case of intervention in those above the threshold) was 7\% for the GDRS and 9\% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs >= 5\% were below \$50,000/QALY gained. Conclusions: Intervening in those with >= 7\% diabetes risk based on the GDRS or >= 9\% on the ARIC 2009 score would be cost-effective. A risk score threshold >= 5\% together with elevated FPG would also allow targeting interventions cost-effectively.},
  language  = {en}
}
@misc{MuehlenbruchKuxhausPencinaetal.2015,
  author    = {M{\"u}hlenbruch, Kristin and Kuxhaus, Olga and Pencina, Michael J. and Boeing, Heiner and Liero, Hannelore and Schulze, Matthias Bernd},
  title     = {A confidence ellipse for the Net Reclassification Improvement},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {825},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42737},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427371},
  pages     = {299 -- 304},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@article{MuehlenbruchKuxhausPencinaetal.2015,
  author    = {M{\"u}hlenbruch, Kristin and Kuxhaus, Olga and Pencina, Michael J. and Boeing, Heiner and Liero, Hannelore and Schulze, Matthias Bernd},
  title     = {A confidence ellipse for the Net Reclassification Improvement},
  series = {European journal of epidemiology},
  volume    = {30},
  journal   = {European journal of epidemiology},
  number    = {4},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0393-2990},
  doi       = {10.1007/s10654-015-0001-1},
  pages     = {299 -- 304},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Muehlenbruch2013,
  author    = {M{\"u}hlenbruch, Kristin},
  title     = {Updating the german diabetes risk score - model extensions, validation and reclassification},
  address   = {Potsdam},
  pages     = {131 S.},
  year      = {2013},
  language  = {en}
}