Joint models for high-dimensional longitudinal data

Longitudinal Data Analysis

Volumn , Issue , 2008, Pages 367-391

  Fieuws, Steffen ^a   Verbeke, Geert ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84885068091     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (84)

1. Agresti, A. (1997). A model for repeated measurements of a multivariate binary response. Journal of the American Statistical Association 92, 315–321.
2. Beckett, L. A., Tancredi, D. J., and Wilson, R. S. (2004). Multivariate longitudinal models for complex change processes. Statistics in Medicine 23, 213–239.
3. Brant, L. J. and Fozard, J. L. (1990). Age changes in pure-tone hearing thresholds in a longitudinal study of normal human aging. Journal of the Acoustical Society of America 88, 813–820.
4. Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.
5. Browne, M. W. (1993). Structured latent curve models. In C. M. Caudras and C. R. Rao (eds.), Multivariate Analysis: Future Directions 2, pp. 171–198. Amsterdam: Elsevier.
6. Browne, M. W. and Du Toit, S. H. C. (1991). Models for learning data. In L. M. Collins and J. L. Horn (eds.), Best Methods for the Analysis of Change: Recent Advances, Unanswered Questions, Future Directions, Chapter 4. Washington, DC: American Psychological Association.
7. Bryk, A. and Raudenbush, S. (1992). Hierarchical Linear Models in Social and Behavioral Research: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
8. Carey, V. and Rosner, B. A. (2001). Analysis of longitudinally observed irregularly timed multivariate outcomes: Regression with focus on cross-component correlation. Statistics in Medicine 20, 21–30.
9. Carey, V., Zeger, S., and Diggle, P. (1993). Modelling multivariate binary data with alternating logistic regressions. Biometrika 80, 517–526.
10. Chakraborty, H., Helms, R. W., Sen, P. K., and Cohen, M. S. (2003). Estimating correlation by using a general linear mixed model: Evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen. Statistics in Medicine 22, 1457–1464.
11. Coull, B. A. and Staudenmayer, J. (2004). Self-modeling regression for multivariate curve data. Statistica Sinica 14, 695–711.
12. Curran, P. J. and Bollen, K. A. (2001). Combining autoregressive and latent curve models. In L. M. Collins and A. G. Sayer (eds.), New Methods for the Analysis of Change, Chapter 4. Washington, DC: American Psychological Association.
13. Dale, J. R. (1986). Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 42, 721–727.
14. Daskalakis, C., Laird, N. M., and Murphy, J. M. (2002). Regression analysis of multiple-source longitudinal outcomes: A Stirling County depression study. American Journal of Epidemiology 155, 88–94.
15. Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data, London: Chapman & Hall.
16. Diggle, P. J., Heagerty, P., Liang, K. Y., and Zeger, S. L. (2002). Analysis of Longitudinal Data, 2nd ed. Oxford: Oxford University Press.
17. Duncan, S. C. and Duncan, T. E. (1996). Multivariate latent growth curve analysis of adolescent substance abuse. Structural Equation Modeling 3, 323–347.
18. Ferrer, E. and McArdle, J. J. (2003). Alternative structural models for multivariate longitudinal data. Structural Equation Modeling 10, 493–524.
19. Fieuws, S. and Verbeke, G. (2004). Joint modelling of multivariate longitudinal profiles: Pitfalls of the random-effects approach. Statistics in Medicine 23, 3093–3104.
20. Fieuws, S. and Verbeke, G. (2006). Pairwise fitting of mixed models for the joint modelling of multivariate longitudinal profiles. Biometrics 62, 424–431.
21. Fieuws, S. (2006). Mixed models for multivariate longitudinal data, (Doctoral Thesis). Leuven: Katholieke Universiteit Leuven (unpublished).
22. Fieuws, S., Verbeke, G., Boen, F., and Delecluse, C. (2006). High-dimensional multivariate mixed models for binary questionnaire data. Applied Statistics 55, 449–460.
23. Fieuws, S., Verbeke G., Maes, B., and Vanrenterghem, Y. (2007). Predicting renal graft failure using multivariate longitudinal profiles. Biostatistics (doi:10.1093/biostatistics/kxm041).
24. Galecki, A. T. (1994). General class of covariance structures for two or more repeated factors in longitudinal data analysis. Communications in Statistics — Theory and Methods 23, 3105–3119.
25. Geys, H., Molenberghs, G., and Ryan, L. (1999). Pseudolikelihood modeling of multivariate outcomes in developmental studies. Journal of the American Statistical Association 94, 734–745.
26. Goldstein, H. (1995). Multilevel Statistical Models. New York: Wiley.
27. Gray, S. M. and Brookmeyer, R. (1998). Estimating a treatment effect from multidimensional longitudinal data. Biometrics 54, 976–988.
28. Gray, S. M. and Brookmeyer, R. (2000). Multidimensional longitudinal data: Estimating a treatment effect from continuous, discrete or time-to-event response variables. Journal of the American Statistical Association 95, 396–406.
29. Gueorguieva, R. (2001). A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Statistical Modelling 1, 177–193.
30. Hancock, G. R., Kuo, W., and Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling 8, 470–489.
31. Heitjan, D. F. and Sharma, D. (1997). Modelling repeated-series longitudinal data. Statistics in Medicine 16, 347–355.
32. Ilk, O. and Daniels, M. (2003). Marginalised transition random effects models for multivariate longitudinal binary data. In Proceedings of the ASA, Section on Biometrics, pp. 1927–1931. Alexandria, VA: American Statistical Association.
33. Jørgensen, B., Lundbye-Christensen, S., Song, P. X. K., and Sun, L. (1996). State-space models for multivariate longitudinal data of mixed types. Canadian Journal of Statistics 24, 385–402 (correction 25, 425).
34. Kiers, H. A. L. and ten Berge, J. M. F. (1994). Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure. British Journal of Mathematical and Statistical Psychology 47, 109–126.
35. Kiers, H. A. L., ten Berge, J. M. F., and Bro, R. (1999). PARAFAC2-Part 1: A direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics 13, 275–294.
36. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963–974.
37. Li, F., Duncan, T. E., Duncan, S. C., and Acock, A. (2001). Latent growth modeling of longitudinal data: A finite growth mixture modeling approach. Structural Equation Modeling 8, 493–530.
38. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.
39. Liang, K. Y. and Zeger, S. L. (1989). A class of logistic regression models for multivariate binary time series. Journal of the American Statistical Association 84, 447–451.
40. Liu, L. C. and Hedeker, D. (2006). A mixed-effects regression model for longitudinal multivariate ordinal data. Biometrics 62, 261–268.
41. Longford, N. T. (1987). A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika 74, 817–827.
42. MacCallum, R., Kim, C., Malarkey, W., and Kiecolt-Glaser, J. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research 32, 215–253.
43. Matsuyama, Y. and Ohashi, Y. (1997). Mixed models for bivariate response repeated measures data using Gibbs sampling. Statistics in Medicine 16, 1587–1601.
44. Marshall, G., De la Cruz-Mésia, R., Baron, A., Rutledge, J. H., and Zerbe, G. O. (2006). Nonlinear random effects model for multivariate responses with missing data. Statistics in Medicine 25(16), 2817–2830.
45. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade and R. B. Catell (eds.), Handbook of Multivariate Experimental Psychology, pp. 561–614, Washington, DC: American Psychological Association.
46. Meredith, W. and Tisak, J. (1990). Latent curve analysis. Psychometrika 55, 107–122.
47. Molenaar, P. C. M. (1985), A dynamic factor model for the analysis of multivariate time series. Psychometrika 50, 181–202.
48. Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
49. Morrell, C. H. and Brant, L. J. (1991). Modelling hearing thresholds in the elderly. Statistics in Medicine 10, 1453–1464.
50. Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika 29, 81–117.
51. Muthén, B. and Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM-algorithm. Biometrics 55, 463–469.
52. Nagin, D. S. (1999). Analysing developmental trajectories: Semi-parametric, group-based approach. Psychological Methods 4, 139–177.
53. Nagin, D. S. and Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology 31, 327–362.
54. Nagin, D. S. and Tremblay, R. E. (2001). Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods 6, 18–34.
55. O’Brien, L. M. and Fitzmaurice, G. M. (2004). Analysis of longitudinal multiple-source binary data using generalized estimating equations. Applied Statistics 53, 177–193.
56. O’Brien, L. M. and Fitzmaurice, G. M. (2005). Regression models for the analysis of longitudinal Gaussian data from multiple sources. Statistics in Medicine 24, 1725–1744.
57. Oort, F. J. (1999). Stochastic three-mode models for mean and covariance structures. British Journal of Mathematical and Statistical Psychology 52, 243–272.
58. Oort, F. J. (2001). Three-mode models for multivariate longitudinal data. British Journal of Mathematical and Statistical Psychology 54, 49–78.
59. Pearson, J. D., Morrell, C. H., Gordon-Salant, S., Brant, L. J., Metter, E. J., Klein, L. L., and Fozard, J. L. (1995). Gender differences in a longitudinal study of age-associated hearing loss. Journal of the Acoustical Society of America 97, 1196–1205.
60. Prentice, R. L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics 44, 1033–1048.
61. Raudenbush, S. W. (2001). Toward a coherent framework for comparing trajectories of individual change. In L. M. Collins and A. G. Sayer (eds.), New Methods for the Analysis of Change, Chapter 2. Washington, DC: American Psychological Association.
62. Reboussin, B. A. and Reboussin, D. M. (1998). Latent transition modeling of progression of healthrisk behavior. Multivariate Behavioral Research 33, 457–478.
63. Reinsel, G. (1982). Multivariate repeated-measurement or growth curve models with multivariate random-effects covariance structure. Journal of the American Statistical Association 77, 190–195.
64. Reinsel, G. (1984). Estimation and prediction in a multivariate random effects generalized linear model. Journal of the American Statistical Association 79, 406–414.
65. Ribaudo, H. J. and Thompson, S. G. (2002). The analysis of repeated multivariate binary quality of life data: A hierarchical model approach. Statistical Methods in Medical Research 11, 69–83.
66. Rochon, J. (1996). Analyzing bivariate repeated measures for discrete and continuous outcome variables. Biometrics 52, 740–750.
67. Rovine, M. J. and Molenaar, P. C. M. (2001). A structural equations modeling approach to the general linear mixed model. In L. M. Collins and A. G. Sayer (eds.), New Methods for the Analysis of Change, Chapter 3. Washington, DC: American Psychological Association.
68. Roy, J. and Lin, X. (2000). Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 56, 1047–1054.
69. Sayer, A. G. and Cumsille, P. E. (2001). Second-order latent growth models. In L. M. Collins and A. G. Sayer (eds.), New Methods for the Analysis of Change, Chapter 6. Washington, DC: American Psychological Association.
70. Shah, A., Laird, N., and Schoenfeld, D. (1997). A random-effects model for multiple characteristics with possibly missing data. Journal of the American Statistical Association 92, 775–779.
71. Shock, N. W., Greullich, R. C., Andres, R., Arenberg, D., Costa, P. T., Lakatta, E. G., and Tobin, J. D. (1984). Normal human aging: The Baltimore Longitudinal Study of Aging. National Institutes of Health publication 84-2450.
72. Sivo, S. A. (2001). Multiple indicator stationary time series models. Structural Equation Modeling 8, 599–612.
73. Stoolmiller, M. (1994). Antisocial behavior, delinquent peer association and unsupervised wandering for boys: Growth and change from childhood to early adolescence. Multivariate Behavioral Research 29, 263–288.
74. Sy, J. P., Taylor, J. M. G., and Cumberland, W. G. (1997). A stochastic model for the analysis of bivariate longitudinal AIDS data. Biometrics 53, 542–555.
75. Ten Have, T. R. and Morabia, A. (1999). Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data. Biometrics 55, 85–93.
76. Thiébaut, R., Jacqmin-Gadda, H., Chêne, G., Leport, C., and Commenges, D. (2002). Bivariate linear mixed models using SAS PROC MIXED. Computer Methods and Programs in Biomedicine, 69, 249–256.
77. Thum, Y.M. (1997). Hierarchical linear models for multivariate outcomes. Journal of Educational and Behavioral Statistics 22, 77–108.
78. Timmerman, M. E. and Kiers, H. A. L. (2002). Three-way component analysis with smoothness constraints. Computational Statistics and Data Analysis 40, 447–470.
79. Timmerman, M. E. and Kiers, H. A. L. (2003). Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika 1, 105–121.
80. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
81. Wang, Y., Guo, W., and Brown, M. B. (2000). Spline smoothing for bivariate data with applications to association between hormones. Statistica Sinica 10, 377–397.
82. Willett, J. B. and Keiley, M. G. (2000). Using covariance structure analysis to model change over time. In H. E. A. Tinsley and S. D. Brown (eds.), Handbook of Applied Multivariate Statistics and Mathematical Modelling, Chapter 23, pp. 665–669, San Diego, CA: Academic Press.
83. Zeng, L. and Cook, R. J. (2004). Transition models for multivariate longitudinal binary data. Working Paper 2004–03, University of Waterloo Working Paper Series. http://www.stats.uwaterloo.ca/statsnavigation/techreports/04WorkingPapers/2004-03.pdf.
84. Zhao, L. P. and Prentice, R. L. (1990). Correlated binary regression using a quadratic exponential model. Biometrika 77, 642–648.