A Bayesian model for the common effects of multiple predictors on mixed outcomes

Interface Focus

Volumn 1, Issue 6, 2011, Pages 886-894

  Weiss, Robert E ^a   Jia, Juan ^a,b   Suchard, Marc A ^a,c,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b AMGEN INC   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian Wald test; Human immunodeficiency virus; Index construction; Multivariate regression; Single index model

Indexed keywords

EID: 84860895729     PISSN: 20428898     EISSN: 20428901     Source Type: Journal    
DOI: 10.1098/rsfs.2011.0041     Document Type: Article

References (40)

1. Travison, T. G. & Brookmeyer, R. 2007 Global effects estimation for multidimensional outcomes. Stat. Med. 26, 4845-4859. (doi:10.1002/sim.2983)
2. Weiss, R. E. 2005 Modeling longitudinal data. New York, NY: Springer.
3. Molenberghs, G. & Verbeke, G. 2005 Models for discrete longitudinal data. New York, NY: Springer.
4. Beckett, L. A., Tancredi, D. J. &Wilson, R. S. 2004 Multivariate longitudinal models for complex change processes. Stat. Med. 23, 231-239. (doi:10.1002/sim.1712)
5. Dubin, J. A. & Müller, H. G. 2005 Dynamical correlation for multivariate longitudinal data. J. Am. Stat. Assoc. 100, 872-881. (doi:10.1198/016214504000001989)
6. Diggle, P. J., Heagerty, P. J., Liang, K. Y. & Zeger, S. L. 2002 Analysis of longitudinal data, 2nd edn. New York, NY: Oxford University Press.
7. Davis, C. S. 2002 Statistical methods for the analysis of repeated measurements. New York, NY: Springer.
8. McCulloch, C. E. & Searle, S. R. 2001 Generalized, linear, and mixed models. New York, NY: Wiley.
9. Nummi, T. & Mottonen, J. 2000 On the analysis of multivariate growth curve. Metrika, 52 77-89. (doi:10.1007/s001840000063)
10. Lin, X., Ryan, L., Sammel, M., Zhang, D., Padungtod, C. & Xu, X. 2000 A scaled linear model for multiple outcomes. Biometrics, 56 593-601. (doi:10.1111/j.0006-341X.2000.00593.x)
11. Verbeke, G. & Molenberghs, G. 2000 Linear mixed models for longitudinal data. New York, NY: Springer.
12. Fieuws, S. & Verbeke, G. 2006 Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics, 62 424-431. (doi:10.1111/j.1541-0420.2006.00507.x)
13. Gray, S. M. & Brookmeyer, R. 2000 Multidimensional longitudinal data: estimating a treatment effect from continuous, discrete or time-to-event response variables. J. Am. Stat. Assoc. 95, 396-406. (doi:10.2307/2669376)
14. Brown, H. & Prescott, R. 1999 Applied mixed models in medicine. New York, NY: Wiley.
15. Gray, S. M. & Brookmeyer, R. 1998 Estimating a treatment effect from multidimensional longitudinal data. Biometrics 54, 976-988. (doi:10.2307/2533850)
16. Littel, R. C., Milliken, G. A., Stroup, W. A. & Wolfinger, R. D. 1996 The SAS system for mixed models. Cary, NC: SAS Institute Inc.
17. Mickey, R. M., Shema, S. J., Vacek, P. M. & Bell, D. Y. 1994 Analysis of multiple outcome variables measured longitudinally. Comput. Stat. Data Anal. 17, 17-33. (doi:10.1016/0167-9473(92)00059-Z)
18. O'Brien, P. C. 1984 Procedures for comparing samples with multiple endpoints. Biometrics 40, 1079-1087. (doi:10.2307/2531158)
19. Little, R. J. A. & Schluchter, M. D. 1985 Maximum likelihood estimation for mixed continuous and categorical data with missing. Biometrika 72, 497-512. (doi:10. 1093/biomet/72.3.497)
20. Sammel, M., Ryan, L. M. & Legler, J. M. 1997 Latent variable models for mixed discrete and continuous outcomes. J. R. Stat. Soc. Ser. B 59, 667-678. (doi:10. 1111/1467-9868.00090)
21. Arminger, G. & Kusters, U. 1988 Latent trait models with indicators of mixed measurement level. In Latent trait and latent class models (eds R. Langeheine & J. Rost). New York, NY: Plenum.
22. Fitzmaurice, G. M. & Laird, N. M. 1997 Regression models for mixed discrete and continuous responses with potentially missing values. Biometrics 53, 110-122. (doi:10. 2307/2533101)
23. Dunson, D. B. 2000 Bayesian latent variable models for clustered mixed outcomes. J. R. Stat. Soc. Ser. B 62, 355-366. (doi:10.1111/1467-9868.00236)
24. Gueorguieva, R. V. & Agresti, A. 2001 A correlated probit model for joint modeling of clustered binary and continuous responses. J. Am. Stat. Assoc. 96, 1102-1112. (doi:10. 1198/016214501753208762)
25. Knaus, W., Draper, E., Wagner, D. & Zimmerman, J. 1985 APACHE II: a severity of disease classification system. Crit. Care Med. 13, 818-829. (doi:10.1097/00003246-198510000-00009)
26. Knaus, W. A. et al. 1991 The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100, 1619-1636. (doi:10. 1378/chest.100.6.1619)
27. Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. 1987 A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40, 373-383. (doi:10.1016/0021-9681 (87)90171-8)
28. Hall, W. H., Ramachandran, R., Narayan, S., Jani, A. B. & Vijayakumar, S. 2004 An electronic application for rapidly calculating Charlson comorbidity score. BMC Cancer 4, 94. (doi:10.1186/1471-2407-4-94)
29. Karnofsky, D. & Burchenal, J. 1949 The clinical evaluation of chemotherapeutic agents in cancer. In Evaluation of chemotherapeutic agents (ed. C. Macleod), pp. 191-205, 2nd edn. New York, NY: Columbia University Press.
30. Oken, M. M., Creech, R. H., Tormey, D. C., Horton, J., Davis, T. E., McFadden, E. T. & Carbone, P. P. 1982 Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am. J. Clin. Oncol. 5, 649-655. (doi:10.1097/00000421-198212000-00014)
31. Nashef, S. A. M, Roques, F., Michel, P., Gauducheau, E., Lemeshow, S., Salamon, R. & EuroSCORE study group. 1999 European system for cardiac operative risk evaluation (EuroSCORE). Eur. J. Cardio-thorac. Surg. 16, 9-13. (doi:10.1016/S1010-7940(99)00134-7)
32. Roques, F., Michel, P., Goldstone, A. R. & Nashef, S. A. M. 2003 Letter to the editor: the logistic EuroSCORE. Eur. Heart J. 24, 1-2. (doi:10.1016/S0195-668X(02)00799-6)
33. Dongen, S. V. 2006 Prior specification in Bayesian statistics: three cautionary tales. J. Theor. Biol. 242, 90-100. (doi:10.1016/j.jtbi.2006.02.002)
34. Kass, R. E. & Wasserman, L. 1996 The selection of prior distributions by formal rules. J. Am. Stat. Assoc. 91, 1343-1370. (doi:10.2307/2291752)
35. Clarke, B. & Wasserman, L. 1993 Noninformative priors and nuisance parameters. J. Am. Stat. Assoc. 88, 1427-1432. (doi:10.2307/2291287)
36. Akaike, H. 1980 The interpretation of improper prior distributions as limits of data dependent proper prior distributions. J. R. Stat. Soc. Ser. B 42, 46-52. See http://www.jstor.org/stable/2984737.
37. Rotheram-Borus, M. J., Murphy, D. A., Swendeman, D., Chao, B., Chabon, B., Zhou, S., Birnbaum, J. & O'Hara, P. 1999 Substance use and its relationship to depression, anxiety, and isolation among youth living with HIV. Int. J. Behav. Med. 6, 293-311. (doi:10.1207/s15327558ij bm0604_1)
38. Rotheram-Borus, M. J., Murphy, D. A., Wight, R. G., Lee, M. B., Lightfoot, M., Swendeman, D., Birnbaum, J. M. & Wright, W. 2001 Improving the quality of life among young people living with HIV. Eval. Program Plann. 24, 227-237. See http://ideas.repec.org/a/eee/epplan/v24y2001i2p227-237.html.
39. Lunn, D. J., Thomas, A., Best, N. & Spiegelhalter, D. 2000 WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10, 325-337. (doi:10.1023/A:1008929526011)
40. Jia, J. & Weiss, R. E. 2009 Common predictor effects for multivariate longitudinal data. Stat. Med. 28, 1793-1804. (doi:10.1002/sim.3589)