A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models.
Measurement errors of medico-technical devices can be separated into systematic bias and random error. We propose a new method to address both simultaneously via generalized additive models for location, scale and shape (GAMLSS) in combination with permutation tests. More precisely, we extend a recently proposed boosting algorithm for GAMLSS to provide a test procedure to analyse potential device effects on the measurements. We carried out a large-scale simulation study to provide empirical evidence that our method is able to identify possible sources of systematic bias as well as random error under different conditions. Finally, we apply our approach to compare measurements of skin pigmentation from two different devices in an epidemiological study.
1 Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Andreas
A
Mayr
2 Institut für Medizinische Biometrie, Informatik und Epidemiologie, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany.
Matthias
M
Schmid
1 Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Annette
A
Pfahlberg
1 Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Wolfgang
W
Uter
1 Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Olaf
O
Gefeller
D000465:Algorithms; D015982:Bias; D003124:Colorimetry / Q000295:instrumentation / Q000379:methods* / Q000592:standards*; D004867:Equipment Design; D004864:Equipment and Supplies; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D012880:Skin Pigmentation
Measurement errors; gradient boosting; permutation test; random error; regression; statistical models; systematic bias