Improving Predictions When Interest Focuses on Extreme Random Effects

References

• Aitken, M., and Wilson, G. T. (1980), “Mixture Models, Outliers and the EM Algorithm,” Technometrics, 22, 325–331. DOI: 10.1080/00401706.1980.10486163.
   Web of Science ®Google Scholar
• Barbour, K. E., Lui, L. Y., McCulloch, C. E., Ensrud, K. E., Cawthon, P. M., Yaffe, K., Barnes, D. E., Fredman, L., Newman, A. B., Cummings, S. R., and Cauley, J. A. (2016), “Trajectories of Lower Extremity Physical Performance: Effects on Fractures and Mortality in Older Women,” Journal of Gerontology Series A: Biological Sciences Medical Sciences, 71, 1609–1615. DOI: 10.1093/gerona/glw071.
   PubMed Web of Science ®Google Scholar
• Davis, J. D., Olsen, M. A., Bommarito, K., LaRue, S. J., Saeed, M., Rich, M. W., and Vader, J. M. (2017), “All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter,” American Journal of Medicine, 130, 9–93.
   Web of Science ®Google Scholar
• Emmert, M., and Schlesinger, M. (2017), “Hospital Quality Reporting in the United States: Does Report Card Design and Incorporation of Patient Narrative Comments Affect Hospital Choice?, Health Services Research, 52, 933–958. DOI: 10.1111/1475-6773.12519.
   PubMed Web of Science ®Google Scholar
• He, Y., Selck, F., and Normand, S. L. (2014), “On the Accuracy of Classifying Hospitals on Their Performance Measures,” Statistics in Medicine, 33, 1081–1103. DOI: 10.1002/sim.6012.
   PubMed Web of Science ®Google Scholar
• Herzog, M. M., Driban, J. B., Cattano, N. M., Cameron, K. L., Tourville, T. W., Marshall, S. W., and Pietrosimone, B. (2017), “Risk of Knee Osteoarthritis Over 24 Months in Individuals Who Decrease Walking Speed During a 12-Month Period: Data from the Osteoarthritis Initiative,” Journal of Rheumatology, 44, 1265–1270. DOI: 10.3899/jrheum.170093.
   PubMed Web of Science ®Google Scholar
• Jones, H. E., and Spiegelhalter, D. J. (2011), “The Identification of “Unusual” Health-Care Providers From a Hierarchical Model,” American Statistician, 65, 154–163.
   Web of Science ®Google Scholar
• Kalbfleisch, J., and Wolfe, R. (2013), “On Monitoring Outcomes of Medical Providers,” Statistics in Biosciences, 5, 286–302. DOI: 10.1007/s12561-013-9093-x.
   Google Scholar
• Lin, T. I., and Lee, J. C. (2008), “Estimation and Prediction in Linear Mixed Models With Skew-Normal Random Effects for Longitudinal Data,” Statistics in Medicine, 27, 1490–1507. DOI: 10.1002/sim.3026.
   PubMed Web of Science ®Google Scholar
• Marafino, B., Park, M., Davies, J., Thombley, R., Sing, D., Luft, H., Sing, D., Kazi, D., Dejong, C., Boscardin, W., Dean, M., and Dudley, R. (2018), “Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data,” JAMA Network Open 1. DOI: 10.1001/jamanetworkopen.2018.5097.
   PubMed Web of Science ®Google Scholar
• McCulloch, C., Searle, S., and Neuhaus, J. (2008), Generalized, Linear and Mixed Models (2nd ed.), New York: Wiley.
   Google Scholar
• Mull, H. J., Chen, Q., O’Brien, W. J., Shwartz, M., Borzecki, A. M., Hanchate, A., and Rosen, A. K. (2013), “Comparing 2 Methods of Assessing 30-Day Readmissions: What is the Impact on Hospital Profiling in the Veterans Health Administration? Medical Care, 51, 589–596. DOI: 10.1097/MLR.0b013e31829019a4.
   PubMed Web of Science ®Google Scholar
• Normand, S.-L., Ash, A., Fienberg, S., Stukel, T., Utts, J., and Louis, T. (2016), “League Tables for Hospital Comparisons,” Annual Review of Statistics and its Application, 3, 21–50. DOI: 10.1146/annurev-statistics-022513-115617.
   Web of Science ®Google Scholar
• Normand, S.-L. T., and Shahian, D. M. (2007), “Statistical and Clinical Aspects of Hospital Outcomes Profiling,” Statistical Science, 22, 206 – 226. DOI: 10.1214/088342307000000096.
   Web of Science ®Google Scholar
• Ohlssen, D. I., Sharples, L. D. and Spiegelhalter, D. J. (2007a), “Flexible Random-Effects Models Using Bayesian Semi-Parametric Models: Applications to Institutional Comparisons,” Statistics in Medicine, 26, 2088–2112. DOI: 10.1002/sim.2666.
   PubMed Web of Science ®Google Scholar
• Ohlssen, D. I., Sharples, L. D. and Spiegelhalter, D. J. (2007b), “A Hierarchical Modelling Framework for Identifying Unusual Performance in Health Care Providers,” Journal of the Royal Statistical Society, Series A, 170, 865–890.
   Web of Science ®Google Scholar
• Shwartz, M., Restuccia, J. D., and Rosen, A. K. (2015), “Composite Measures of Health Care Provider Performance: A Description of Approaches,” Milbank Quarterly, 93, 788–825. DOI: 10.1111/1468-0009.12165.
   PubMed Web of Science ®Google Scholar
• Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., Brach, J., Chandler, J., Cawthon, P., Connor, E. B., Nevitt, M., Visser, M., Kritchevsky, S., Badinelli, S., Harris, T., Newman, A. B., Cauley, J., Ferrucci, L., and Guralnik, J. (2011), “Gait Speed and Survival in Older Adults,” JAMA, 305, 50–58.
   PubMed Web of Science ®Google Scholar
• Tokdar, S. T., and Kass, R. E. (2010), “Importance Sampling: A Review,” WIREs Computational Statistics, 2, 54–60. DOI: 10.1002/wics.56.
   Google Scholar
• U.S. Centers for Medicare and Medicaid Services (2017), “Outcome Measures,” Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/OutcomeMeasures.html. Accessed: 2018-2-27.
   Google Scholar
• Verbeke, G., and Lesaffre, E. (1997), “The Effect of Misspecifying the Random-Effects Distribution in Linear Mixed Models for Longitudinal Data,” Computational Statistics & Data Analysis, 23, 541–556.
   Web of Science ®Google Scholar
• Zhang, D., and Davidian, M. (2001), “Linear Mixed Models With Flexible Distribution of Random Effects for Longitudinal Data,” Biometrics, 57, 795–802. DOI: 10.1111/j.0006-341x.2001.00795.x.
   PubMed Web of Science ®Google Scholar
• Zhang, P., Luo, D., Li, P., Sharpsten, L., and Medeiros, F. A. (2015), “Log-Gamma Linear-Mixed Effects Models for Multiple Outcomes With Application to a Longitudinal Glaucoma Study,” Biomedical Journal, 57, 766–776.
   Google Scholar
• Zhang, P., Song, P. X., Qu, A., and Greene, T. (2008), “Efficient Estimation for Patient-Specific Rates of Disease Progression Using Nonnormal Linear Mixed Models,” Biometrics, 64, 29–38. DOI: 10.1111/j.1541-0420.2007.00824.x.
   PubMed Web of Science ®Google Scholar