Estimation and testing for multiple regulation of multivariate mixed outcomes

Biometrics

Volumn 72, Issue 4, 2016, Pages 1194-1205

  Agniel, Denis ^a   Liao, Katherine P ^b   Cai, Tianxi ^c  

^a HARVARD MEDICAL SCHOOL   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Familywise error control; Hierarchical lasso; Multiple phenotypes; Multiple regulation; Multiple testing; Resampling; Semiparametric models; Stepdown testing

Indexed keywords

EPIDEMIOLOGICAL STUDIES; ERROR CONTROL; FAMILYWISE ERROR CONTROL; HIERARCHICAL LASSO; MULTIPLE PHENOTYPE; MULTIPLE REGULATION; MULTIPLE TESTING; RESAMPLING; SEMI-PARAMETRIC MODELING; STEPDOWN TESTING;

SAMPLING;

EPIDEMIOLOGY; ERROR ANALYSIS; ETIOLOGY; GENOMICS; MULTIVARIATE ANALYSIS; PARAMETER ESTIMATION; PHENOTYPE; TESTING METHOD; UNCERTAINTY ANALYSIS;

AUTOANTIBODY;

BIOMETRY; COMPUTER SIMULATION; HUMAN; METHODOLOGY; MULTIVARIATE ANALYSIS; PHENOTYPE; PROCEDURES; SAMPLE SIZE; STATISTICAL BIAS; STATISTICAL MODEL;

AUTOANTIBODIES; BIAS (EPIDEMIOLOGY); BIOMETRY; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; PHENOTYPE; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84959378577     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12495     Document Type: Article

References (26)

1. Cai, T., Wei, L., and Wilcox, M. (2000). Semiparametric regression analysis for clustered failure time data. Biometrika 87, 867–878.
2. Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96, 1348–1360.
3. He, Q., Avery, C. L., and Lin, D.-Y. (2013). A general framework for association tests with multivariate traits in large-scale genomics studies. Genetic Epidemiology 37, 759–767.
4. Jiang, C. and Zeng, Z.-B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111–1127.
5. Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.
6. Liao, K. P., Cai, T., Gainer, V., Goryachev, S., Zeng-treitler, Q., Raychaudhuri, S., et al. (2010). Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care & Research 62, 1120–1127.
7. Minnier, J., Tian, L., and Cai, T. (2011). A perturbation method for inference on regularized regression estimates. Journal of the American Statistical Association 106, 1371–1382.
8. Murphy, S. A. and Van der Vaart, A. W. (2000). On profile likelihood. Journal of the American Statistical Association 95, 449–465.
9. Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D. Y., Pollack, J. R., and Wang, P. (2010). Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. The Annals of Applied Statistics 4, 53–77.
10. Romano, J. P. and Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association 100, 94–108.
11. Romano, J. P., and Wolf, M. (2010). Balanced control of generalized error rates. The Annals of Statistics 38, 598–633.
12. Schifano, E. D., Li, L., Christiani, D. C., and Lin, X. (2013). Genome-wide association analysis for multiple continuous secondary phenotypes. The American Journal of Human Genetics 92, 744–759.
13. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., and Smoller, J. W. (2013). Pleiotropy in complex traits: Challenges and strategies. Nature Reviews Genetics.
14. Taylor, K. E., Chung, S. A., Graham, R. R., Ortmann, W. A., Lee, A. T., Langefeld, C. D., et al. (2011). Risk alleles for systemic lupus erythematosus in a large case-control collection and associations with clinical subphenotypes. PLoS Genetics 7, e1001311.
15. Thomas, R., Have, T., Kunselman, A. R., Pulkstenis, E. P., and Landis, J. R. (1998). Mixed effects logistic regression models for longitudinal binary response data with informative drop-out. Biometrics 54, 367–383.
16. Tian, L., Cai, T., Goetghebeur, E., and Wei, L. (2007). Model evaluation based on the sampling distribution of estimated absolute prediction error. Biometrika 94, 297–311.
17. Turlach, B. A., Venables, W. N., and Wright, S. J. (2005). Simultaneous variable selection. Technometrics 47, 349–363.
18. Uno, H., Cai, T., Tian, L., and Wei, L. (2007). Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 102, 527–537.
19. van Heel, D. A., Franke, L., Hunt, K. A., Gwilliam, R., Zhernakova, A., and Inouye, M., (2007). A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nature Genetics 39, 827–829.
20. Wang, H. and Leng, C. (2007). Unified lasso estimation by least squares approximation. Journal of the American Statistical Association 102, 1039–1048.
21. Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B 68, 49–67.
22. Zeng, D. and Lin, D. (2007). Maximum likelihood estimation in semiparametric regression models with censored data. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 507–564.
23. Zhang, H. H. and Lu, W. (2007). Adaptive lasso for cox's proportional hazards model. Biometrika 94, 691–703.
24. Zhernakova, A., van Diemen, C. C., and Wijmenga, C. (2009). Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nature Reviews Genetics 10, 43–55.
25. Zhou, N. and Zhu, J. (2010). Group variable selection via a hierarchical lasso and its oracle property. arXiv preprint arXiv:1006.2871.
26. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American statistical association 101, 1418–1429.