TY  - JOUR
A1  - Schütt, Heiko Herbert
A1  - Harmeling, Stefan
A1  - Macke, Jakob H.
A1  - Wichmann, Felix A.
T1  - Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
JF  - Vision research : an international journal for functional aspects of vision.
N2  - The psychometric function describes how an experimental variable, such as stimulus strength, influences the behaviour of an observer. Estimation of psychometric functions from experimental data plays a central role in fields such as psychophysics, experimental psychology and in the behavioural neurosciences. Experimental data may exhibit substantial overdispersion, which may result from non-stationarity in the behaviour of observers. Here we extend the standard binomial model which is typically used for psychometric function estimation to a beta-binomial model. We show that the use of the beta-binomial model makes it possible to determine accurate credible intervals even in data which exhibit substantial overdispersion. This goes beyond classical measures for overdispersion goodness-of-fit which can detect overdispersion but provide no method to do correct inference for overdispersed data. We use Bayesian inference methods for estimating the posterior distribution of the parameters of the psychometric function. Unlike previous Bayesian psychometric inference methods our software implementation-psignifit 4 performs numerical integration of the posterior within automatically determined bounds. This avoids the use of Markov chain Monte Carlo (MCMC) methods typically requiring expert knowledge. Extensive numerical tests show the validity of the approach and we discuss implications of overdispersion for experimental design. A comprehensive MATLAB toolbox implementing the method is freely available; a python implementation providing the basic capabilities is also available. (C) 2016 The Authors. Published by Elsevier Ltd.
KW  - Psychometric function
KW  - Bayesian inference
KW  - Beta-binomial model
KW  - Overdispersion
KW  - Non-stationarity
KW  - Confidence intervals
KW  - Credible intervals
KW  - Psychophysical methods
Y1  - 2016
U6  - https://doi.org/10.1016/j.visres.2016.02.002
SN  - 0042-6989
SN  - 1878-5646
VL  - 122
SP  - 105
EP  - 123
PB  - Elsevier
CY  - Oxford
ER  - 
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  -