A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity

Biometrics

Volumn 67, Issue 1, 2011, Pages 280-289

  Neelon, Brian ^a   O'Malley, A James ^b   Normand, Sharon Lise T ^b,c  

^a DUKE UNIVERSITY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Bayesian analysis; Growth mixture model; Latent class model; Mental health parity; Semi continuous data; Two part model

Indexed keywords

HUMAN RESOURCE MANAGEMENT; RANDOM PROCESSES;

BAYESIAN ANALYSIS; CONTINUOUS DATA; GROWTH MIXTURE MODEL; LATENT CLASS MODEL; MENTAL HEALTH; MENTAL HEALTH PARITY; MIXTURE MODELING; SEMI-CONTINUOUS; SEMI-CONTINUOUS DATA; TWO-PART MODEL;

PROBABILITY DENSITY FUNCTION;

BAYESIAN ANALYSIS; EXPENDITURE; HEALTH INSURANCE; MENTAL HEALTH; PROBABILITY;

EID: 79952594101     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01439.x     Document Type: Article

References (34)

1. Beunckens, C., Molenberghs, G., Verbeke, G., and Mallinckrodt, C. (2008). A latent-class mixture model for incomplete longitudinal Gaussian data. Biometrics 64, 96-105.
2. Celeux, G., Forbes, F., Robert, C. P., and Titterington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis 1, 651-674.
3. Congdon, P. (2005). Bayesian Models for Categorical Data. Chichester, U.K. John Wiley and Sons.
4. Elliott, M. R., Gallo, J. J., Ten Have, T. R., Bogner, H. R., and Katz, I. R. (2005). Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction. Biostatistics 6, 119-143.
5. Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Annals of Statistics 1, 209-230.
6. Frühwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models. New York Springer.
7. Garrett, E. S. and Zeger, S. L. (2000). Latent class model diagnosis. Biometrics 56, 1055-1067.
8. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd edition. Boca Raton, Florida Chapman and Hall.
9. Gelman, A., Meng, X. L., and Stern, H. S. (1996). Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica 6, 733-807.
10. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics Vol. 4, J. M. Bernardo, J. Berger, A. P. Dawid, and A.F.M. Smith (eds), 169-193. Cambridge, U.K. Oxford University Press.
11. Ghosh, P. and Albert, P. S. (2009). A Bayesian analysis for longitudinal semi-continuous data with an application to an acupuncture clinical trial. Computational Statistics & Data Analysis 53, 699-706.
12. Goldman, H. H., Frank, R. G., Burnam, M. A., Huskamp, H. A., Ridgely, S., Normand, S.-L. T., Young, A. S., Berry, C. L., Azzone, V., Busch, A. B., Azrin, S. T., Moran, G., Lichtenstein, C., and Blasinksy, M. (2006). Behavioral health insurance parity for federal employees. The New England Journal of Medicine 354, 1378-1386.
13. Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711-732.
14. Leiby, B. E., Sammel, M. D., Ten Have, T. R., and Lynch, K. G. (2009). Identification of multivariate responders and non-responders by using Bayesian growth curve latent class models. Journal of the Royal Statistical Society, Series C 58, 505-524.
15. Lenk, P. J. and DeSarbo, W. S. (2000). Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika 65, 93-119.
16. Lin, H., McCulloch, C. E., Turnbull, B. W., Slate, E. H., and Clark, L. C. (2000). A latent class mixed model for analyzing biomarker trajectories with irregularly scheduled observations. Statistics in Medicine 19, 1303-1318.
17. Lin, H., McCulloch, C. E., Turnbull, B. W., and Slate, E. H. (2002). Latent class models for joint analysis of longitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer. Journal of the American Statistical Association 97, 53-65.
18. Muthén, B. and Shedden K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55, 463-469.
19. Muthén, B., Brown, C. H., Booil Jo, K. M., Khoo, S.-T., Yang, C.-C., Wang, C.-P., Kellam, S. G., Carlin, J. B., and Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics 3, 459-475.
20. Neelon, B. H., O'Malley, A. J., and Normand, S.-L. T. (2010). A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use. To appear in Statistical Modelling.
21. Olsen, M. K. and Schafer, J. L. (2001). A two-part random-effects model for semi-continuous longitudinal data. Journal of the American Statistical Association 96, 730-745.
22. Proust-Lima, C., Letenneur L., and Jacqmin-Gaddam, H. (2007). A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Statistics in Medicine 26, 2229-2245.
23. Proust-Lima, C., Joly, P., Dartigues, J.-F., and Jacqmin-Gaddam, H. (2009). Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach. Computational Statistics & Data Analysis 53, 1142-1154.
24. R Development Core Team. (2008). R:A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria ISBN 3-900051-07-0. .
25. Richardson, S. (2002). Discussion of Spiegelhalter TTTTT. Journal of the Royal Statistical Society, Series B 64, 626-627.
26. Smith, B. J. (2007). boa: An R package for MCMC output convergence assessment and posterior inference. Journal of Statistical Software 21, 1-37.
27. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-639.
28. Spiegelhalter, D. J., Thomas, A., Best, N., and Lunn, D. (2003). Cambridge, U.K. Medical Research Council Biostatistics Unit .
29. Stephens, M. (2000). Dealing with label switching in mixture models. Journal of the Royal Statistical Society, Series B 62, 795-809.
30. Su, L., Tom, B. D. M., and Farewell, T. (2009). Bias in 2-part mixed models for longitudinal semicontinuous data. Biostatistics 10, 374-389.
31. Tooze, J. A., Grunwald, G. K., and Jones, R. H. (2002). Analysis of repeated measures data with clumping at zero. Statistical Methods in Medical Research 11, 341-355.
32. U.S. Office of Personnel Management. (2000). Call letter for contract year 2001: Policy guidance. FEHB Program carrier letter no. 2000-17. Washington, D.C. OPM .
33. Verbeke, G. and Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. Journal of the American Statistical Association 91, 217-221.
34. White, J. W., Standish, J. D., Thorrold, S. R., and Warner, R. R. (2008). Markov chain Monte Carlo methods for assigning larvae natal sites using natural geochemical tags. Ecological Applications 18, 1901-1913.