A multi-response multilevel model with application in nurse care coordination

References

• Ahn, M., Zhang, H.H. and Lu, W. (2012) Moment-based method for random effects selection in linear mixed models. Statistica Sinica, 22(4), 1539–1562.
   Web of Science ®Google Scholar
• Aiken, L.H., Clarke, S.P., Sloane, D.M., Lake, E.T. and Cheney, T. (2008) Effects of hospital care environment on patient mortality and nurse outcomes. The Journal of Nursing Administration, 38(5), 223–229.
   PubMed Web of Science ®Google Scholar
• Anderson, G. (2007) Chronic conditions: Making the case for ongoing care, in Chronic Care Chartbook. Johns Hopkins Bloomberg School of Public Health: Baltimore, MD, pp. 1–74.
   Google Scholar
• Bondell, H.D., Krishna, A. and Ghosh, S.K. (2010) Joint variable selection for fixed and random effects in linear mixed- effects models. Biometrics, 66(4), 1069–1077.
   PubMed Web of Science ®Google Scholar
• Burnham, K.P. and Anderson, D.R. (2002) Model selection and multimodel inference: A practical information-theoretic approach, Springer Science & Business Media, Germany.
   Google Scholar
• Burnham, K.-P. and Anderson, D.R. (2004) Multimodel inference understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304.
   Web of Science ®Google Scholar
• Chen, Z. and Dunson, D.B. (2003) Random effects selection in linear mixed models. Biometrics, 59(4), 762–769.
   PubMed Web of Science ®Google Scholar
• Demidenko, E. (2013) Mixed Models: Theory and Applications with R. John Wiley & Sons, Hoboken, NJ.
   Google Scholar
• Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.
   Web of Science ®Google Scholar
• Duva, I.H. (2010) Factors impacting staff nurse care coordination. Ph.D. Thesis, Emory University, Atlanta, GA.
   Google Scholar
• Fahrmeir, L., Kneib, T. and Konrath, S. (2010) Bayesian regularisation in structured additive regression: A unifying perspective on shrinkage, smoothing and predictor selection. Statistics and Computing, 20(2), 203–219.
   Web of Science ®Google Scholar
• Fan, J. and Li, R. (2001) Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348–1360.
   Web of Science ®Google Scholar
• George, E.I. and McCulloch, R.E. (1997) Approaches for Bayesian variable selection. Statistica Sinica, 7(2), 339–373.
   Web of Science ®Google Scholar
• Huang, S., Li, J., Lamb, G., Schmitt, M. H., and Fowler, J. (2014) Multiple data sources fusion for enterprise quality improvement by a multilevel latent response model. IIE Transactions, 46(5), 512–525.
   Web of Science ®Google Scholar
• Ibrahim, J.G., Zhu, H., Garcia, R.I. and Guo, R. (2011) Fixed and random effects selection in mixed effects models. Biometrics, 67(2), 495–503.
   PubMed Web of Science ®Google Scholar
• Institute for Healthcare Improvement. (2004) Transitions Home How-To Guide Innovation Series 2004. Available from: Institute for Healthcare Improvement: Cambridge, MA.
   Google Scholar
• Lamb, G.S., Schmitt, M.H., Edwards, P., Sainfort, F., Duva, I. and Higgins, M. (2008) Measuring staff nurse care coordination in the hospital. Invited presentation at the National State of the Science Congress on Nursing Research, Washington, DC.
   Google Scholar
• Laschinger, H.K.S. and Leiter, M.P. (2006) The impact of nursing work environments on patient safety outcomes: The mediating role of burnout engagement. Journal of Nursing Administration, 36(5), 259–267.
   PubMed Web of Science ®Google Scholar
• McDonald, K.M., Sundaram, V., Bravata, D.M., Lewis, R., Lin, N., Kraft, S.A., … Owens, D.K. (2007). Care Coordination. In K. Shojania, K. McDonald, R. Wachter & D. Owens (Eds.), Closing the quality gap: A critical analysis of quality improvement strategies (pp. 1–158). Rockville, MD: Agency for Healthcare Research and Quality.
   Google Scholar
• Medicare Payment Advisory Commission. (2005) Report to Congress: Promoting Greater Efficiency in Medicare, Washington, DC.
   Google Scholar
• Page, A. (ed). (2004) Keeping Patients Safe:: Transforming the Work Environment of Nurses. National Academies Press, Washington, DC.
   Google Scholar
• Schelldorfer, J., Bühlmann, P. and Van de Geer, S. (2011) Estimation for high‐dimensional linear mixed‐effects models using ℓ1‐penalization. Scandinavian Journal of Statistics, 38(2), 197–214.
   Web of Science ®Google Scholar
• Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2013) A sparse-group lasso. Journal of Computational and Graphical Statistics, 22(2), 231–245.
   Web of Science ®Google Scholar
• Stricker, K.H., Kimberger, O., Schmidlin, K., Zwahlen, M., Mohr, U., and Rothen, H.U. (2009) Family satisfaction in the intensive care unit: What makes the difference? Intensive care medicine, 35(12), 2051.
   Web of Science ®Google Scholar
• Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288.
   Web of Science ®Google Scholar
• Wang, S., Nan, B., Rosset, S. and Zhu, J. (2011) Random lasso. The Annals of Applied Statistics, 5(1), 468–485.
   PubMed Web of Science ®Google Scholar
• Wu, C.J. (1983) On the convergence properties of the EM algorithm. The Annals of Statistics, 11(1), 95–103.
   Web of Science ®Google Scholar
• Yang, Y. (2005) Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrika, 92(4), 937–950.
   Web of Science ®Google Scholar
• Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49–67.
   Web of Science ®Google Scholar
• Zhao, P., Rocha, G. and Yu, B. (2009) The composite absolute penalties family for grouped and hierarchical variable selection. The Annals of Statistics, 37(6A), 3468–3497.
   Web of Science ®Google Scholar
• Zhao, P. and Yu, B. (2006) On model selection consistency of lasso. The Journal of Machine Learning Research, 7, 2541–2563.
   Web of Science ®Google Scholar
• Zhou, N. and Zhu, J. (2010) Group variable selection via a hierarchical lasso and its oracle property. arXiv preprint arXiv:1006.2871.
   Google Scholar
• Zou, H. (2006) The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429.
   Web of Science ®Google Scholar
• Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.
   Web of Science ®Google Scholar