Premium
Classification of Therapy Resistance Based on Longitudinal Biomarker Profiles
Author(s) -
Kohlmann Mareike,
Held Leonhard,
Grunert Veit Peter
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200800157
Subject(s) - linear discriminant analysis , statistics , biomarker , discriminant , artificial intelligence , selection (genetic algorithm) , computer science , calibration , pattern recognition (psychology) , mathematics , machine learning , biology , biochemistry
To classify patients either as resistant or non‐resistant to HIV therapy based on longitudinal viral load profiles, we applied longitudinal quadratic discriminant analysis and examined various measures, mainly derived from the Brier Score, to assess the biomarker performance in terms of discrimination and calibration. The analysis of the application data revealed an increase in performance by using longer profiles instead of single biomarker measurements. Simulations showed that the selection of mixed models for the estimation of the group‐specific discriminant rule parameters should be based on BIC, rather than on the best performance measure. An incorrect model selection can lead to spuriously better or worse performance as misclassification and classification certainty regards, especially with increasing length of the profiles and for more complex models with random slopes.