High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components

References

• Breiman L. Random forests. Mach Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Green SB, Byar DP. The effect of stratified randomization on size and power of statistical tests in clinical trials. J Chronic Dis. 1978;31:445–454. doi: 10.1016/0021-9681(78)90008-5
   PubMedGoogle Scholar
• Fan JJ, Su XG, Levine RA, et al. Trees for correlated survival data by goodness of split, With applications to Tooth Prognosis. J Am Stat Assoc. 2006;101:959–967. doi: 10.1198/016214506000000438
   Web of Science ®Google Scholar
• Fan JJ, Su XG, Nunn ME. Multivariate exponential survival trees and their application to tooth prognosis. Comput Stat Data Anal. 2009;53:1110–1121. doi: 10.1016/j.csda.2008.10.019
   PubMed Web of Science ®Google Scholar
• Su XG, Fan JJ. Multivariate survival trees: A maximum likelihood approach based on frailty models. Biometrics. 2004;60:93–99. doi: 10.1111/j.0006-341X.2004.00139.x
   PubMed Web of Science ®Google Scholar
• Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–748.
   PubMed Web of Science ®Google Scholar
• Neuhaus J, Kalbfleisch J. Between- and within-cluster covariate effects in the analysis of clustered data. Biometrics. 1998;54:638–645. doi: 10.2307/3109770
   PubMed Web of Science ®Google Scholar
• Neuhaus J, McCulloch C. Separating between- and within-cluster covariate effects by using conditional and partitioning methods. J R Stat Soc Ser B (Stat Methodol). 2006;68:859–872. doi: 10.1111/j.1467-9868.2006.00570.x
   Google Scholar
• Sjölander A, Lichtenstein P, Larsson H, et al. Between-within models for survival analysis. Stat Med. 2013;32:3067–3076. doi: 10.1002/sim.5767
   PubMed Web of Science ®Google Scholar
• Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests. Ann Appl Stat. 2008;2:841–860. doi: 10.1214/08-AOAS169
   Web of Science ®Google Scholar
• Ishwaran H, Kogalur UB, Gorodeski EZ, et al. High-dimensional variable selection for survival data. J Am Stat Assoc. 2010;105:205–217. doi: 10.1198/jasa.2009.tm08622
   Web of Science ®Google Scholar
• Ishwaran H. Variable importance in binary regression trees and forests. Electron J Stat. 2007;1:519–537. doi: 10.1214/07-EJS039
   Web of Science ®Google Scholar
• Altmann A, Tolosi L, Sander O, et al. Permutation importance: A corrected feature importance measure. Bioinformatics. 2010;26:1340–1347. doi: 10.1093/bioinformatics/btq134
   PubMed Web of Science ®Google Scholar
• Tsiatis A. A nonidentifiability aspect of the problem of competing risks. Proc National Acad Sci. 1975;72:20–22. doi: 10.1073/pnas.72.1.20
   PubMed Web of Science ®Google Scholar
• Leung K, Elashoff RM, Afifi AA. Censoring issues in survival analysis. Annu Rev Public Health. 1997;18:83–104. doi: 10.1146/annurev.publhealth.18.1.83
   PubMed Web of Science ®Google Scholar
• Feng C, Wang H, Tu XM. Power loss of stratified log-rank test in homogeneous samples. Int J Qual Stat Reliab. 2010: 4. doi:10.1155/2010/942184.
   Google Scholar
• Wei LJ, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J Am Stat Assoc. 1989;84:1065–1073. doi: 10.1080/01621459.1989.10478873
   Web of Science ®Google Scholar
• Su XG, Fan JJ, Wang A, et al. On simulating multivariate failure times. Int J Appl Math Stat. 2006;5:8–18.
   Google Scholar
• Emrich LJ, Piedmonte MR. A method for generating high-dimensional multivariate binary variates. Am Stat. 1991;45:302–304.
   Web of Science ®Google Scholar
• Centers for Disease Control and Prevention (CDC). National Center for Health Statistics. National Hospital Discharge Survey: 2010 table, Procedures by selected patient characteristics - Number by procedure category and age. CDC web site. http://www.cdc.gov/nchs/fastats/inpatient-surgery.htm. 2013.
   Google Scholar
• Strobl C, Boulesteix AL, Kneib T, et al. Conditional variable importance for random forests. BMC Bioinf. 2008;9:307. doi: 10.1186/1471-2105-9-307
   PubMed Web of Science ®Google Scholar
• Gail MH, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika. 1984;71:431–444. doi: 10.1093/biomet/71.3.431
   Web of Science ®Google Scholar
• Aalen OO. Heterogeneity in survival analysis. Stat Med. 1988;7:1121–1137. doi: 10.1002/sim.4780071105
   PubMed Web of Science ®Google Scholar
• Reid S, Tibshirani R. Regularization Paths for conditional Logistic regression: TheclogitL1Package. J Stat Softw. 2014;58:1–23. doi: 10.18637/jss.v058.i12
   Web of Science ®Google Scholar