Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO

Biometrics

Volumn 63, Issue 1, 2007, Pages 259-271

  Datta, Susmita ^a   Le Rademacher, Jennifer ^b   Datta, Somnath ^a  

^a UNIVERSITY OF LOUISVILLE   (United States)

^b UNIVERSITY OF GEORGIA   (United States)

Author keywords

Cancer; Gene expression; Partial least squares; Right censoring; Survival

Indexed keywords

DATA HANDLING; DISEASES; FORECASTING; LEAST SQUARES APPROXIMATIONS; TUMORS;

CANCER; COVARIATES; FAILURE TIME; GENES EXPRESSION; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; MICROARRAYS DATA; PARTIAL LEAST-SQUARES; PERFORMANCE; RIGHT CENSORING; SURVIVAL;

GENE EXPRESSION;

ARRAY; CANCER; GENE EXPRESSION; MODELING; PREDICTION; REGRESSION ANALYSIS; SURVIVAL;

CANCER PATIENT; CANCER SURVIVAL; CLINICAL RESEARCH; DATA ANALYSIS; HUMAN; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR; LUNG CANCER; MICROARRAY ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; REGRESSION ANALYSIS; REVIEW; SAMPLE SIZE; SIMULATION; SURVIVAL TIME;

EID: 34247259498     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00660.x     Document Type: Review

References (34)

1. Bair, E. and Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data. PLoS Biology 2, 511-522.
2. Beer, D. G., Kardia, S. L., Huang, C., et al. (2002). Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine 8, 816-824.
3. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289-300.
4. Brown, P. J. (1993). Measurement, Regression, and Calibration. New York: Oxford University Press.
5. Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika 66, 429-436.
6. Bullinger, L., D̈ohner, K., Bair, E., Fr̈ohling, S., Schlenk, R. F., Tibshirani, R., D̈ohner, H., and Pollack, J. R. (2004). Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. New England Journal of Medicine 350, 1605-1616.
7. Datta, S. (2001). Exploring relationships in gene expressions: A partial least squares approach. Gene Expression 9, 249-255.
8. Datta, S. (2005). Estimating the mean life time using right censored data. Statistical Methodology 2, 65-69.
9. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004). Least angle regression (with discussions). Annals of Statistics 32, 407-499.
10. Frank, I. E. and Friedman, J. H. (1993). A statistical view of some chemometrics regression tools (with discussion). Technometrics 35, 109-148.
11. Geladi, P. (1992). Wold, Herman, the father of PLS. Chemometrics and Intelligent Laboratory Systems 15, 1, R7-R8.
12. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag.
13. Huang, J. and Harrington, D. (2005). Iterative partial least squares with right-censored data analysis: A comparison to other dimension reduction techniques. Biometrics 61, 17-24.
14. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York: Wiley.
15. Kiss, H., Yang, Y., Kiss, C., Andersson, K., Klein, G., Imreh, S., and Dumansk, J. P. (2002). The transcriptional map of the common eliminated region 1. European Journal of Human Genetics 10, 52-61.
16. Knight, K. and Fu, W. (2000). Asymptotics for Lasso-type estimators. Annals of Statistics 28, 1356-1378.
17. Koul, H., Susarla, V., and Van Ryzin, J. (1981). Regression analysis with randomly right-censored data. Annals of Statistics 9, 1276-1288.
18. Li, H. and Gui, J. (2004). Partial Cox regression analysis for high-dimensional microarray gene expression data. Bioinformatics 20 (suppl. 1), i208-i215.
19. Lossos, I. S., Czerwinski, D. K., Alizadeh, A. A., Wechser, M. A., Tibshirani, R., Botstein, D., and Levy, R. (2004). Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. New England Journal of Medicine 350, 1814-1816.
20. Martens, H. and Naes, T. (1989). Multivariate Calibration. New York: Wiley.
21. Marx, B. D. (1996). Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics 38, 374-381.
22. Nguyen, D. V. and Rocke, D. M. (2002a). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39-50.
23. Nguyen, D. V. and Rocke, D. M. (2002b). Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics 18, 1625-1632.
24. Park, P. J., Tian, L., and Kohane, I. S. (2002). Linking gene expression data with patient survival times using partial least squares. Bioinformatics 18, 120-127.
25. Purohit, P. and Rocke, D. M. (2003). Discriminant models for high-throughput proteomics mass spectrometer data. Proteomics 3, 1699-1703.
26. Robins, J. and Finkelstein, D. (2000). Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 56, 779-788.
27. Robins, J. M. and Rotnitzky, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS Epidemiology - Methodological Issues, N. Jewell, K. Dietz, and V. Farewell (eds), 297-331. Boston: Birkhauser.
28. Satten, G. A. and Datta, S. (2001). The Kaplan-Meier estimator as an inverse-probability-of-censoring weighted average. American Statistician 55, 207-210.
29. Satten, G. A., Datta, S., and Robins, J. M. (2001). An estimator for the survival function when data are subject to dependent censoring. Statistics and Probability Letters 54, 397-403.
30. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267-288.
31. Tibshirani, R. (1997). The lasso method for variable selection in the Cox model. Statistics in Medicine 16, 385-395.
32. Tobias, R. D. (1997). An Introduction to Partial Least Squares Regression. TS-509. Cary, North Carolina: SAS Institute. Available at http://support.sas.com/techsup/technote/ts509.pdf.
33. Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In Multivariate Analysis, P. R. Krishnaiah (ed), 391-420. New York: Academic Press.
34. Wold, H. (1975). Soft modeling by latent variables: The nonlinear iterative partial least squares (NIPALS) approach. In Perspectives in Probability and Statistics, Papers in Honour of M. S. Bartlett, J. Gani (ed), 117-144. London: Academic Press.