Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients

Biometrics

Volumn 70, Issue 1, 2014, Pages 153-163

  Qian, Jing ^a,b   Payabvash, Seyedmehdi ^c   Kemmling, André ^c   Lev, Michael H ^c   Schwamm, Lee H ^c   Betensky, Rebecca A ^b  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

AUC; Cerebral infarction; Conditional logistic regression; Elastic net; Lasso; Penalized likelihood; ROC analysis

Indexed keywords

BRAIN; NEUROIMAGING; REGRESSION ANALYSIS;

AUC; CEREBRAL INFARCTION; CONDITIONAL LOGISTIC REGRESSIONS; ELASTIC NET; LASSO; MASSACHUSETTS; PENALIZED LIKELIHOOD; PENALIZED-LIKELIHOOD; ROC ANALYSIS; STROKE PATIENTS;

HOSPITALS;

BRAIN; COVARIANCE ANALYSIS; EPIDEMIOLOGY; GENOME; HEALTH RISK; HOSPITAL SECTOR; PNEUMONIA; REGRESSION ANALYSIS; STATISTICAL ANALYSIS;

MASSACHUSETTS; UNITED STATES;

ALACHLOR;

ADOLESCENT; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BRAIN INFARCTION; CASE CONTROL STUDY; CEREBROVASCULAR ACCIDENT; CHILD; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; CONDITIONAL LOGISTIC REGRESSION; CROSS INFECTION; ELASTIC TISSUE; HUMAN; MALE; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; PENALIZED LIKELIHOOD; PNEUMONIA; PREVALENCE; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL; UNITED STATES; VERY ELDERLY; YOUNG ADULT;

AUC; CEREBRAL INFARCTION; CONDITIONAL LOGISTIC REGRESSION; ELASTIC NET; LASSO; PENALIZED LIKELIHOOD; ROC ANALYSIS;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; BOSTON; CASE-CONTROL STUDIES; CHILD; COMPUTER SIMULATION; CROSS INFECTION; HUMANS; LOGISTIC MODELS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PNEUMONIA; PREVALENCE; PROSPECTIVE STUDIES; STROKE; TOMOGRAPHY, X-RAY COMPUTED; YOUNG ADULT;

EID: 84895894429     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12113     Document Type: Article

References (25)

1. Adewale, A. J., Dinu, I., and Yasui, Y. (2010). Boosting for correlated binary classification. Journal of Computational and Graphical Statistics 19, 140-153.
2. Balasubramanian, R., Houseman, E., Coull, B., Lev, M., Schwamm, L., and Betensky, R. (in press). Variable importance in matched case-control studies in settings of high dimensional data. Journal of the Royal Statistical Society, Series C.
3. Breslow, N. E. and Day, N. E. (1980). Statistical Methods in Cancer Research: The Analysis of Case-control Studies, Vol. 1. Lyon, France: International Agency for Research on Cancer.
4. Cai, J., Fan, J., Li, R., and Zhou, H. (2005). Variable selection for multivariate failure time data. Biometrika 92, 303-316.
5. Davatzikos, C., Resnick, S., Wu, X., Parmpi, P., and Clark, C. (2008). Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. NeuroImage 41, 1220-1227.
6. Davenport, R., Dennis, M., Wellwood, I., and Warlow, C. (1996). Complications after acute stroke. Stroke 27, 415-420.
7. Duchesne, S., Caroli, A., Geroldi, C., Collins, D. L., and Frisoni, G. B. (2009). Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. NeuroImage 47, 1363-1370.
8. Fan, J. and Lv, J. (2010). A selective overview of variable selection in high dimensional feature space. Statistica Sinica 20, 101-148.
9. Hilker, R., Poetter, C., Findeisen, N., Sobesky, J., Jacobs, A., Neveling, M., et al. (2003). Nosocomial pneumonia after acute stroke: Implications for neurological intensive care medicine. Stroke 34, 975-981.
10. Katzan, I. L., Dawson, N. V., Thomas, C. L., Votruba, M. E., and Cebul, R. D. (1998). The cost of pneumonia after acute stroke. Neurology 68, 1938-1943.
11. Lin, D. Y. and Ying, Z. (1993). Cox regression wigh incomplete covariate measurements. Journal of the American Statistical Association 88, 1341-1349.
12. Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics 29, 479-486.
13. Martin, R., Goodyear, B., Gati, J., and Menon, R. (2001). Cerebral cortical representation of automatic and volitional swallowing in humans. Journal of Neurophysiology 85, 938-950.
14. Meier, L., van de Geer, S., and Bühlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society, Series B 70, 53-71.
15. Prentice, R. L. and Pyke, R. (1979). Logistic disease incidence models and case-control studies. Biometrika 66, 403-412.
16. Rothman, K. J., Greenland, S., and Lash, T. L. (2008). Modern Epidemiology, 3rd edition. Philadelphia, PA: Lippincott Williams & Wilkins.
17. Ruczinski, I., Kooperberg, C., and Leblanc, M. (2003). Logic regression. Journal of Computational and Graphical Statistics 12, 475-511.
18. Tan, Q., Thomassen, M., and Kruse, T. A. (2007). Feature selection for predicting tumor metastases in microarray experiments using paired design. Cancer Informatics 3, 213-218.
19. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267-288.
20. Tworoger, S., Rosner, B., Willett, W., and Hankinson, S. (2011). The combined influence of multiple sex and growth hormones on risk of postmenopausal breast cancer: A nested case-control study. Breast Cancer Research 13, R99.
21. Upadya, A., Thorevska, N., Sena, K., Manthous, C., and Amoateng-Adjepong, Y. (2004). Predictors and consequences of pneumonia in critically ill patients with stroke. The Journal of Critical Care 19, 16-22.
22. Woodward, M. (2005). Epidemiology: Study Design and Data Analysis, 2nd edition. Boca Raton, FL: Chapman & Hall/CRC.
23. Wu, J., Devlin, B., Ringquist, S., Trucco, M., and Roeder, K. (2010). Screen and clean: A tool for identifying interactions in genome-wide association studies. Genetic Epidemiology 34, 275-285.
24. Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., and Lange, K. (2009). Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25, 714-721.
25. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 67, 301-320.