Classification of therapy resistance based on longitudinal biomarker profiles

Biometrical Journal

Volumn 51, Issue 4, 2009, Pages 610-626

  Kohlmann, Mareike ^a   Held, Leonhard ^b   Grunert, Veit Peter ^c  

^a UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF ZURICH   (Switzerland)

^c ROCHE DIAGNOSTICS GMBH   (Germany)

Author keywords

Brier Score; Covariance structure; Discriminant analysis; Linear mixed effects models; Longitudinal data

Indexed keywords

BIOMARKERS; PATIENT TREATMENT;

APPLICATION DATA; BRIER SCORE; COVARIANCE STRUCTURES; HIV THERAPY; LINEAR MIXED-EFFECTS MODEL; LOAD PROFILES; LONGITUDINAL DATA; PERFORMANCE; QUADRATIC DISCRIMINANT ANALYSIS; VIRAL LOAD;

DISCRIMINANT ANALYSIS;

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; BIOLOGICAL MARKER;

ANTIVIRAL RESISTANCE; ARTICLE; BLOOD; DISCRIMINANT ANALYSIS; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; LONGITUDINAL STUDY; METHODOLOGY; OUTCOME ASSESSMENT; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

ANTI-HIV AGENTS; BIOLOGICAL MARKERS; DISCRIMINANT ANALYSIS; DRUG RESISTANCE, VIRAL; HIV INFECTIONS; HUMANS; LONGITUDINAL STUDIES; OUTCOME ASSESSMENT (HEALTH CARE); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

EID: 70350707421     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.200800157     Document Type: Article

References (29)

1. Diamond, G. A. (1992). What price perfection? Calibration and discrimination of clinical prediction models. Journal of Clinical Epidemiology 45, 85-89.
2. Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, vol. 57. CRC Press, Boca Raton.
3. Galecki, A. T. (1994). General-class of covariance-structures for 2 or more repeated factors in longitudinal data-analysis. Communications in Statistics-Theory and Methods 23, 3105-3119.
4. Gerds, T. A., Cai, T. and Schumacher, M. (2008). The performance of risk prediction models. Biometrical Journal 50, 457-479.
5. Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102, 359-378.
6. Gradstein, M. (1986). Maximal correlation between normal and dichotomous variables. Journal of Educational Statistics 11, 259-261.
7. Gurney, J. W. and Swensen, S. J. (1995). Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. Radiology 196, 823-829.
8. Hand, D. J. (1997). Construction and Assessment of Classification Rules. Wiley, New York.
9. Harrell, F. E. (2006). How to avoid overfitting in training optimization with respect to area under ROC. Available online at http://sci4um.com/about33215.html.
10. James, G. M. and Hastie, T. J. (2001). Functional linear discriminant analysis for irregularly sampled curves. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, 533-550.
11. Johnson, R. A. and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ.
12. Littell, R. C., Pendergast, J. and Natarajan, R. (2000). Modelling covariance structure in the analysis of repeated measures data. Statistics in Medicine 19, 1793-1819.
13. Marshall, G. and Barón, A. E. (2000). Linear discriminant models for unbalanced longitudinal data. Statistics in Medicine 19, 1969-1981.
14. May, S. and DeGruttola, V. (2007). Nonparametric tests for two-group comparisons of dependent observations obtained at varying time points. Biometrics 63, 194-200.
15. McLachlan, G. J. (1992). Discriminant Analysis and Statistical Pattern Recognition. Wiley, New York.
16. Müller, H. G. (2005). Functional modelling and classification of longitudinal data. Scandinavian Journal of Statistics 32, 223-240.
17. Murphy, A. H. (1973). A new vector partition of the probability score. Journal of Applied Meteorology 12, 595-600.
18. Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, New York.
19. Pfeiffer, R. M. and Bura, E. (2008). A model free approach to combining biomarkers. Biometrical Journal 50, 558-570.
20. R Development Core Team. (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0. Available online at http://www.R-project.org
21. Sanders, F. (1963). On subjective probability forecasting. Journal of Applied Meteorology 2, 191-201.
22. Shah, A., Laird, N. and Schoenfeld, D. (1997). Random-effects model for multiple characteristics with possibly missing data. Journal of the American Statistical Association 92, 775-779.
23. Shao, J. (1993). Linear model selection by cross-validation. Journal of the American Statistical Association 88, 486-494.
24. Spiegelhalter, D. J. (1986). Probabilistic prediction in patient management and clinical trials. Statistics in Medicine 5, 421-433.
25. Tomasko, L., Helms, R. W. and Snapinn, S. M. (1999). A discriminant analysis extension to mixed models. Statistics in Medicine 18, 1249-1260.
26. Weiss, R. (2005). Modeling Longitudinal Data. Springer, New York.
27. Yates, J. (1982). External correspondence: decompositions of the mean probability score. Organizational Behavior and Human Performance 30, 132-156.
28. Zhou, X. H., Obuchowski, N. A. and McClish, D. K. (2002). Statistical Methods in Diagnostic Medicine. Wiley, New York.
29. Zolg, J. W. and Langen, H. (2004). How industry is approaching the search for new diagnostic markers and biomarkers. Molecular and Cellular Proteomics 3, 345-354.