L 1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets

Statistics in Medicine

Volumn 31, Issue 14, 2012, Pages 1464-1474

  Archer, K J ^a   Williams, A A A ^a  

^a VIRGINIA COMMONWEALTH UNIVERSITY   (United States)

Author keywords

Continuation ratio; Gene expression analysis; L 1 penalization; Microarray; Ordinal response

Indexed keywords

AKAIKE INFORMATION CRITERION; ARTICLE; BAYESIAN INFORMATION CRITERION; GENE EXPRESSION; L1 PENALIZED CONTINUATION RATIO MODEL; MICROARRAY ANALYSIS; ORDINAL RESPONSE; PREDICTION; PREDICTOR VARIABLE; SAMPLE SIZE; SIMULATION; STATISTICAL CONCEPTS; STATISTICAL MODEL; BLOOD; COMPUTER SIMULATION; CROHN DISEASE; GENE EXPRESSION PROFILING; GLUCOSE BLOOD LEVEL; HUMAN; MALE; METABOLISM; NON INSULIN DEPENDENT DIABETES MELLITUS; PROSTATE TUMOR; STATISTICAL ANALYSIS; STATISTICS; ULCERATIVE COLITIS;

BLOOD GLUCOSE; COLITIS, ULCERATIVE; COMPUTER SIMULATION; CROHN DISEASE; DATA INTERPRETATION, STATISTICAL; DIABETES MELLITUS, TYPE 2; GENE EXPRESSION PROFILING; HUMANS; MALE; MODELS, STATISTICAL; PROSTATIC NEOPLASMS;

EID: 84862002828     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4484     Document Type: Article

References (33)

1. Theise ND. Liver biopsy assessment in chronic viral hepatitis: a personal, practical approach. Modern Pathology 2007; 20 Suppl 1:S3-S14.
2. Hosmer DW, Lemeshow S. Applied Logistic Regression, (2nd edn). John Wiley & Sons: Hoboken NJ, 2000.
3. Agresti A. Categorical Data Analysis. John Wiley & Sons: Hoboken, NJ, 2002.
4. Wu B. Differential gene expression detection using penalized linear regression models: the improved SAM statistics. Bioinformatics 2005; 21:1565-1571.
5. Wu B. Differential gene expression detection and sample classification using penalized linear regression models. Bioinformatics 2006; 22:472-476.
6. Zhu J, Hastie T. Classification of gene microarrays by penalized logistic regression. Biostatistics 2004; 5:427-443.
7. Schimek MG. Penalized binary regression for gene expression profiling. Methods of Information in Medicine 2004; 43:439-444.
8. Schumacher M, Binde H, Gerds T. Assessment of survival prediction models based on microarray data. Bioinformatics 2007; 23:1768-1774.
9. Gui J, Li H. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 2005; 21:3001-3008.
10. Li H, Gui J. Partial Cox regression analysis for high-dimensional microarray gene expression data. Bioinformatics 2004; 20 Suppl 1:i208-i215.
11. Shedden K, Taylor JMG, Enkemann SA, Tsao MS, Yeatman TJ, Gerald WL, Eschrich S, Jurisica I, Giordano TJ, Misek DE, Chang AC, Zhu CQ, Strumpf D, Hanash S, Shepherd FA, Ding K, Seymour L, Naoki K, Pennell N, Weir B, Verhaak R, Ladd-Acosta C, Golub T, Gruidl M, Sharma A, Szoke J, Zakowski M, Rusch V, Kris M, Viale A, Motoi N, Travis W, Conley B, Seshan VE, Meyerson M, Kuick R, Dobbin KK, Lively T, Jacobson JW, Beer DG. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nature Medicine 2008; 14:822-827.
12. Kiiveri HT. A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations. BMC Bioinformatics 2008; 9:195.
13. O'Connell AA. Logistic Regression Models for Ordinal Response Variables. Sage Publications: Thousand Oaks CA, 2006.
14. Cole SR, Ananth CV. Regression models for unconstrained, partially or fully constrained continuation odds ratios. International Journal of Epidemiology 2001; 30:1379-1382.
15. Greenland S. Alternative models for ordinal logistic regression. Statistics in Medicine 1994; 13:1665-1677.
16. Bender R, Benner A. Calculating ordinal regression models in SAS and S-Plus. Biometrical Journal 2000; 42:677-699.
17. Cox C. Multinomial regression models based on continuation ratios. Statistics in Medicine 1988; 7:435-441.
18. Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, B 1996; 58:267-288.
19. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 1997; 16:385-395.
20. Park MY, Hastie T. L1-regularization path algorithm for generalized linear models. Journal of the Royal Statistical Society, B 2007; 64:659-677.
21. Friedman JH, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 2010; 33:1-22.
22. Lokhorst J, Venables B, Turlach B, Maechler M. lasso2: L1 constrained estimation aka 'lasso', 2010., Date accessed Janaurary 10, 2011.
23. R Core Development Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2009.
24. Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SD, Kalyana-Sundaram S, Wei JT, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM. Integrative molecular concept modeling of prostate cancer progression. Nature Genetics 2007; 39:41-51.
25. Burczynski ME, Peterson RL, Twine NC, Zuberek KA, Brodeur BJ, Casciotti L, Maganti V, Reddy PS, Strahs A, Immermann F, Spinelli W, Schwertschlag U, Slager AM, Cotreau MM, Dorner AJ. Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. Journal of Molecular Diagnostics 2006; 8:51-61.
26. Noguchi E, Homma Y, Kang X, Netea MG, Ma X. A Crohn's disease-associated NOD2 mutation suppresses transcription of human IL10 by inhibiting activity of the nuclear ribonucleoprotein hnRNP-A1. Nature Immunology 2009; 10:471-479.
27. Bolignano D, Della Toree A, Lacquaniti A, Costantino G, Fries W, Buemi M. Neutrophil gelatinase-associated lipocalin levels in patients with Crohn disease undergoing treatment with infliximab. Journal of Investigational Medicine 2010; 58:569-571.
28. Arisawa T, Tahara T, Shibata T, Nagasaka M, Nakamura M, Kamiya Y, Fujita H, Yoshioka D, Okubo M, Sakata M, Wang FY, Hirata I, Nakano H. Nrf2 gene promoter polymorphism is associated with ulcerative colitis in a Japanese population. Hepatogastroenterology 2008; 55:394-397.
29. Hokari R, Kurihara C, Nagata N, Aritake K, Okada Y, Watanabe C, Komoto S, Nakamura M, Kawaguchi A, Nagao S, Urade Y, Miura S. Increased expression of lipocalin-type-prostaglandin D synthase in ulcerative colitis and exacerbating role in murine colitis. American Journal of Physiological: Gastrointestinal and Liver Physiology 2011; 300:G401-G408.
30. Ananth CV, Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. International Journal of Epidemiology 1997; 26:1323-1333.
31. Thomson D, Furness RW, Monaghan P. The analysis of ordinal response data in the behavioural sciences. Animal Behaviour 1998; 56:1041-1043.
32. Cawley G, Talbot N. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 2006; 22:2348-2355.
33. CSIRO Bioinformatics. HGfns: Sparse model fitting, 2010. R package version 3.0.8.