Hierarchical Generalized Linear Models in the Analysis of Variations in Health Care Utilization

Journal of the American Statistical Association

Volumn 94, Issue 445, 1999, Pages 29-42

  Daniels, Michael J ^a   Gatsonis, Constantine ^b  

^a Iowa State University ^*  (United States)

^b BROWN UNIVERSITY   (United States)

Author keywords

Health services research; Markov chain Monte Carlo; Multilevel model

Indexed keywords

EID: 0442325063     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.1999.10473816     Document Type: Article

References (65)

1. Albert, J. H. (1988), “Bayesian Estimation of Poisson Means Using a Hierarchical Log-Linear Model,” in Bayesian Statistics 3, eds. J. M. Bernardo, M. H. DeGroot, D. V. Lindley, and A. F. M. Smith, Oxford, U.K.: Clarendon Press, pp. 519-531.
2. Albert, J. H., and Chib, S. (1996), “Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models,” Journal of the American Statistical Association, 92, 916-925.
3. Bennet, J. E., Racine-Poon, A., and Wakefield, J. C. (1996), “MCMC for Nonlinear Hierarchical Models,” in Markov Chain Monte Carlo in Practice, eds. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, London: Chapman and Hall, pp. 339-358.
4. Besag, J., Green, P., Higdon, D., and Mengersen, K. (1995), “Bayesian Computation and Stochastic Systems,” Statistical Science, 10, 3-41.
5. Breslow, N., and Clayton, D. (1993), “Approximate Inference in Generalized Linear Models,” Journal of the American Statistical Association, 88, 9-25.
6. Bryk, A. S., and Raudenbush, S. W. (1992), Hierarchical Linear Models: Application and Data Analysis Methods, Newbury Park, CA: Sage.
7. Bryk, A. S., Raudenbush, S. W., and Congdon, R. T. (1998), Hierarchical Linear Modeling, Version 4.03, Chicago, IL: Scientific Software International.
8. Calvin, J. A., and Sedransk, J. (1991), “Bayesian and Frequentist Predictive Inference for the Patterns of Care Studies,” Journal of the American Statistical Association, 86, 36-48.
9. Christiansen, C. L., and Morris, C. N. (1997), “Hierarchical Poisson Regression Modeling,” Journal of the American Statistical Association, 92, 618-632.
10. Cowles, M. K., and Carlin, B. P. (1996), “Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review,” Journal of the American Statistical Association, 91, 883-904.
11. Daniels, M. J., and Gatsonis, C. G. (1997), “Hierarchical Polytomous Regression Models With Applications to Health Services Research,” Statistics in Medicine, 16, 2311-2326.
12. Dartmouth Medical School, Center for the Evaluative Clinical Sciences (1996), The Dartmouth Atlas of Health Care in the United States, Chicago: American Hospital Publishing.
13. DiCiccio, T. J., Kass, R. E., Raftery, A., and Wasserman, L. (1997), “Computing Bayes Factors by Combining Simulation and Asymptotic Approximations,” Journal of the American Statistical Association, 92, 903-915.
14. Diehr, P. (1984), “Small Area Statistics: Large Statistical Problems,” American Journal of Public Health, 74, 313-314.
15. Diehr, P., Cain, K., Connell, F., and Volinn, E. (1990), “What Is Too Much Variation? The Null Hypothesis in Small Area Analysis,” Health Services Research, 24, 741-771.
16. Escobar, M. D., and West, M. (1995), “Bayesian Density Estimation and Inference Using Mixtures,” Journal of the American Statistical Association, 90, 577-588.
17. Gatsonis, C., Epstein, A., Newhouse, J., Normand, S., and McNeil, B. (1995), “Variations in the Utilization of Coronary Angiography for Elderly Patients With an Acute Myocardial Infarction: An Analysis Using Hierarchical Logistic Regression,” Medical Care, 33, 625-642.
18. Gatsonis, C., Normand, S. L., Liu, C., and Morris, C. (1993), “Geographic Variation of Procedure Utilization: A Hierarchical Model Approach,” Medical Care, 31, YS54-YS59.
19. Gelfand, A. E., Dey, D. K., and Chang, H. (1995), “Model Determination Using Predictive Distributions With Implementation via Sampling-Based Methods,” in Bayesian Statistics 4, eds. J. M. Bernardo, J. O. Berger, A. P. David, and A. F. M. Smith, Oxford, U.K.: Clarendon Press, pp. 147-167.
20. Gelfand, A. E., and Ghosh, S. K. (1998), “Model Choice: A Minimum Posterior Predictive Loss Approach,” Biometrika, 85, 1-12.
21. Gelfand, A. E., Sahu, S. K., and Carlin, B. P. (1995), “Efficient Parameter-izations for Normal Linear Mixed Models,” Biometrika, 82, 479-488.
22. Gelfand, A. E., and Smith, A. F. M. (1990), “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of the American Statistical Association, 85, 398-409.
23. Gelman, A., Meng, X-L., and Stem, H. (1996), “Posterior Predictive Assessment of Model Fitness via Realized Discrepancies,” Statistica Sinica, 6, 733-807.
24. Gelman, A., and Rubin, D. B. (1992), “Inference From Iterative Simulation Using Multiple Sequences,” Statistical Science, 7, 457-472.
25. Ghosh, M., Natarajan, K., Stroud, T. W. F., and Carlin, B. P. (1998), “Generalized Linear Models for Small-Area Estimation,” Journal of the American Statistical Association.
26. Gilks, W. R., Wang, C. C., Yvonnet, B., and Coursaget, P. (1993), “Random Effect Models for Longitudinal Data Using Gibbs Sampling,” Biometrics, 49, 441-454.
27. Goldstein, H. (1995), Multilevel Statistical Models, London: Edward Arnold.
28. Goldstein, H., and Spiegelhalter, D. (1996), “League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance,” Journal of the Royal Statistical Society, Ser. A, 159, 385-444.
29. Jacquier, E., Poison, N., and Rossi, P. (1994), “Bayesian Analysis of Stochastic Volatility Models,” Journal of Business and Economic Statistics, 12, 371-389.
30. Kahn, M. J., and Raftery, A. E. (1996), “Discharge Rates of Medicare Stroke Patients to Skilled Nursing Facilities: Bayesian Logistic Regression With Unobserved Heterogeneity,” Journal of the American Statistical Association, 91, 29-41.
31. Kass, R. E., and Raftery, A. E. (1995), “Bayes Factors,” Journal of the American Statistical Association, 90, 773-795.
32. Kass, R. E., and Steffey, D. (1989), “Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models),” Journal of the American Statistical Association, 84, 717-726.
33. Kass, R. E., and Wasserman, L. (1995), “A Reference Bayesian Test for Nested Hypotheses and Its Relationship to the Schwarz Criterion,” Journal of the American Statistical Association, 90, 928-934.
34. Keeler, E., Rubestein, L., Kahn, K., Draper, D., Harrison, E., McGinty, M., Rogers, W., and Brook, R. (1992), “Hospital Characteristics and Quality of Care,” Journal of the American Medical Association, 268, 1709-1714.
35. Khattree, R., and Naik, D. N. (1995), Applied Multivariate Statistics With SAS Software, Cary, NC: SAS Institute, Inc.
36. Laird, N. M., and Ware, J. H. (1982), “Random Effects Models for Longitudinal Data,” Biometrics, 38, 963-974.
37. Laud, P. W., and Ibrahim, J. G. (1995), “Predictive Model Selection,” Journal of the Royal Statistical Society, Ser. B, 57, 247-262.
38. Lee, Y., and Nelder, J. A. (1996), “Hierarchical Generalized Linear Models” (with discussion), Journal of the Royal Statistical Society, Ser. B, 58, 619-678.
39. Lin, X., and Breslow, N. E. (1996), “Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion,” Journal of the American Statistical Association, 91, 1007-1016.
40. Lindstrom, M. J., and Bates, D. M. (1988), “Newton-Raphson and E-M Algorithms for Linear Mixed Effect Models for Repeated Measures Data,” Journal of the American Statistical Association, 83, 1014-1022.
41. Liu, C. (1996), “Bayesian Robust Multivariate Linear Regression With Incomplete Data,” Journal of the American Statistical Association, 91, 1219-1227.
42. Longford, N. T. (1987), “A Fast Scoring Algorithm for Maximum Likelihood Estimation in Unbalanced Mixed Models With Nested Random Effects,” Biometrika, 74, 817-827.
43. Malec, D., Sedransk, J., Moriarity, C. L., and LeClere, F. B. (1997), “Small Area Inference for Binary Variables in the National Health Interview Survey,” Journal of the American Statistical Association, 92, 815-826.
44. McClellan, M., McNeil, B. J., and Newhouse, J. P. (1994), “Does More Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce Mortality? Analysis Using Instrumental Variables,” Journal of the American Medical Association, 272, 859-866.
45. McCullagh, P., and Nelder, J. (1989), Generalized Linear Models, London: Chapman and Hall.
46. McNeil, B., Pedersen, S., and Gatsonis, C. (1992), “Current Issues in Profiling Quality of Care,” Inquiry, 29, 298-307.
47. Natarajan, K., Ghosh, M., and Maiti, T. (1998), “Hierarchical Bayes Quality Measurement Plan,” unpublished manuscript submitted to Communications in Statistics: Simulation and Computation.
48. Normand, S. L., Glickman, M., and Gatsonis, C. (1997a), “Statistical Methods for Profiling Providers: Issues and Applications,” Journal of the American Statistical Association, 92, 803-814.
49. Normand, S. L., Glickman, M. E., and Ryan, T. (1997b), “Modeling Mortality Rates for Elderly Heart Attack Patients: Profiling Hospitals in the Cooperative Cardiovascular Project,” in Case Studies in Bayesian Statistics, eds: C. Gatsonis, J. Hodges, R. Kass, and N. Singpurwalla, New York: Springer-Verlag, pp. 155-236.
50. O'Hagan, A. (1994), Kendall's Advanced Theory of Statistics, Vol. 2B: Bayesian Inference, New York: Wiley.
51. Pashos, C. L., Newhouse, J. P., and McNeil, B. J. (1993), “Temporal Changes in the Care and Outcomes of Elderly Patients With Acute Myocardial Infarction, 1987 Through 1990,” Journal of the American Medical Association, 270, 1832-1837.
52. Paul-Shaheen, P., Clark, J. D., and Williams, D. (1987), “Small Area Analysis: A Review and Analysis of the North American Literature,” Journal of Health Politics, Policy and Law, 14, 741-809.
53. Rubin, D. B. (1988), “Using the SIR Algorithm to Simulate Posterior Distributions,” in Bayesian Statistics 3, eds. J. M. Bernardo, M. H. DeGroot, D. V. Lindley, and A. F. M. Smith, Oxford, U.K.: Clarendon Press, pp. 395-402.
54. Smith, A. F. M., and Roberts, G. O. (1993), “Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the Royal Statistical Society, Ser. B, 55, 3-23.
55. Spiegelhalter, D. J., Best, N. G., Gilks, W. R., and Inskip, H. (1996), “Hepatitis B: A Case Study in MCMC Methods,” in Markov Chain Monte Carlo in Practice, eds. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, London: Chapman and Hall, pp. 21-44.
56. Tierney, L. (1994), “Markov Chains for Exploring Posterior Distributions,” The Annals of Statistics, 22, 1701-1762.
57. Udvarhelyi, I. S., Gatsonis, C., Epstein, A., Pashos, C., Newhouse, J. P., and McNeil, B. J. (1992), “Acute Myocardial Infarction in the Medicare Population: Process of Care and Clinical Outcomes,” Journal of the American Medical Association, 18, 2530-2536.
58. Verdinelli, I., and Wasserman, L. (1995), “Computing Bayes Factors Using the Savage-Dickey Density Ratio,” Journal of the American Statistical Association, 90, 614-618.
59. Weiss, R. E. (1996), “Bayesian Model Checking with Applications to Hierarchical Models,” technical report, UCLA School of Public Health, Dept, of Biostatistics.
60. Wennberg, J., and Gittelsohn, A. (1982), “Variations in Medical Care Among Small Areas,” Scientific American, 246, 120-134.
61. Wolfe, R., and Becker, M. (1994), “Modeling Utilization Rates,” technical report, University of Michigan, Dept, of Biostatistics.
62. Wong, G., and Mason, W. (1985), “The Hierarchical Logistic Regression Model for Multilevel Analysis,” Journal of the American Statistical Association, 80, 513-524.
63. Wong, G., and Mason, W. (1991), “Contextually Specific Effects and Other Generalizations of the Hierarchical Linear Model for Comparative Analysis,” Journal of the American Statistical Association, 86, 487-503.
64. Zeger, S., and Karim, M. (1991), “Generalized Linear Models With Random Effects: A Gibbs Sampling Approach,” Journal of the American Statistical Association, 86, 79-86.
65. Zeger, S., Liang, K., and Albert, P. (1988), “Models for Longitudinal Data: A Generalized Estimating Equations Approach,” Biometrics, 44, 1049-1066.