Can matching improve the performance of boosting for identifying important genes in observational studies?

Computational Statistics

Volumn 28, Issue 1, 2013, Pages 37-49

  Reiser, Veronika ^a,b   Porzelius, Christine ^a,b   Stampf, Susanne ^a,b   Schumacher, Martin ^b   Binder, Harald ^b,c  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

^c UNIVERSITY MEDICAL CENTER MAINZ   (Germany)

Author keywords

Boosting; Gene selection; High dimensional observational data; Matching

Indexed keywords

EID: 84874213197     PISSN: 09434062     EISSN: 16139658     Source Type: Journal    
DOI: 10.1007/s00180-012-0306-4     Document Type: Article

References (30)

1. Binder H, Schumacher M (2008a) Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinf 9: 14.
2. Binder H, Schumacher M (2008b) Comment on 'network-constrained regularization and variable selection for analysis of genomic data'. Bioinformatics 24(21): 2566-2568.
3. Binder H, Schumacher M (2008c) Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Stat Appl Genet Mol Biol 7(1): 12.
4. Binder H, Porzelius C, Schumacher M (2009) Rank-based p-values for sparse high-dimensional risk prediction models fitted by componentwise boosting, FDM-Preprint Nr. 101.
5. Boulesteix A-L, Hothorn T (2010) Testing the additional predictive value of high-dimensional data. BMC Bioinf 11: 78.
6. Bourquin J et al (2006) Identification of distinct molecular phenotypes in acute megakaryoblastic leukemia by gene expression profiling. PNAS 103(9): 3339-3344.
7. Breiman L (2001) Random forests. Mach Learn 45: 5-32.
8. Brier G (1950) Verification of forecast expressed in terms of probability. Mon Weather Rev 78(1): 1-3.
9. Cepeda MS et al (2003) Optimal matching with a variable number of controls vs. a fixed number of controls for a cohort study: trade-offs. J Clin Epidemiol 56: 230-237.
10. Chiaretti S et al (2004) Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103: 2771-2778.
11. Cochran W, Rubin D (1973) Controlling bias in observational studies: a review. Indian J Stat Ser A 35(4): 417-446.
12. Cristianini N, Shawe-Taylor J (1999) An introduction to SVM. Cambridge University Press, Cambridge.
13. Dettling M, Bühlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19: 1061-1069.
14. Gu X, Rosenbaum P (1993) Comparison of multivariable matching methods: structures, distances and algorithms. J Comput Graph Stat 2: 405-420.
15. Hansen B (2004) Full matching in an observational study coaching for the SAT. J Am Stat Assoc 99(467): 609-618.
16. Heller R et al (2009) Matching methods for observational microarray studies. Bioinformatics 25(7): 904-909.
17. Hummel M et al (2008) GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics 24(1): 78-85.
18. Ming K, Rosenbaum P (2000) Substantial gains in bias reduction from matching with a variable number of controls. Biometrics 56(1): 118-124.
19. Rosenbaum P, Rubin D (1985) The bias due to incomplete matching. Biometrics 41: 103-116.
20. Rosenbaum P (1989) Optimal matching for observational studies. J Am Stat Assoc 84(408): 1024-1032.
21. Rubin D (1973) Matching to remove bias in observational studies. Biometrics 29(1): 159-183.
22. Rubin D (1979) Using multivariable matched sampling and regression adjustment to control bias in observational studies. J Am Stat Assoc 74: 318-324.
23. Rubin D (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.
24. Simon R et al (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95(1): 14-18.
25. Smith H (1997) Matching with multiple controls to estimate treatment effects in observational studies. Sociol Methodol 27(1): 325-353.
26. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3 (Article 3).
27. Thomas JG et al (2001) An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genom Res 11: 1227-1236.
28. Tusher VG et al (2001) Significant analysis of microarrays applied to the ioonizing radiation response. Proc Natl Acad Sci USA 98: 5116-5121.
29. Tutz G, Binder H (2007) Boosting ridge regression. Comput Stat Data Anal 51(12): 6044-6059.
30. Vapnik V (1995) The nature of statistical learning theory. Springer, New York.