Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach

Computational Statistics and Data Analysis

Volumn 53, Issue 4, 2009, Pages 1142-1154

  Proust Lima, Cécile ^a,b   Joly, Pierre ^a,b   Dartigues, Jean François ^a,b   Jacqmin Gadda, Hélène ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TRANSFORMATIONS; MAXIMUM LIKELIHOOD ESTIMATION;

CONDITIONAL INDEPENDENCES; ESTIMATION PROCEDURES; MAXIMUM PENALIZED LIKELIHOOD; NON-LINEAR TRANSFORMATIONS; PARAMETRIC FUNCTIONS; PROPORTIONAL HAZARD MODELS; SIMULATION STUDIES; STATISTICAL SOFTWARE;

RISK PERCEPTION;

EID: 58749099434     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.10.017     Document Type: Article

References (28)

1. Bandeen-Roche K., Miglioretti D.L., Zeger S.L., and Rathouz P.J. Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association 92 440 (1997) 1375-1386
2. Bauer D.J., and Curran P.J. Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods 8 3 (2003) 338-363
3. Benton A. Manuel pour l'application du Test de Rétention Visuelle. Applications cliniques et expérimentales. 2nd edition (1965), Centre de Psychologie appliquée, Paris
4. Dobson A., and Henderson R. Diagnostics for joint longitudinal and dropout time modeling. Biometrics 59 (2003) 741-751
5. Fletcher R. Practical Methods of Optimization. second ed. (2000), John Wiley & Sons, New-York (Chapter 3 edition)
6. Hawkins D.S., Allen D.M., and Stromberg A.J. Determining the number of components in mixtures of linear models. Computational Statistics and Data Analysis 38 1 (2001) 15-48
7. Henderson R., Diggle P., and Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics 1 4 (2000) 465-480
8. Isaacs B., and Kennie A.T. The Set test as an aid to the detection of dementia in old people. British Journal of Psychiatry 123 575 (1973) 467-470
9. Jacqmin-Gadda H., Fabrigoule C., Commenges D., and Dartigues J.F. A 5-year longitudinal study of the Mini-Mental State Examination in normal aging. American Journal of Epidemiology 145 6 (1997) 498-506
10. Joly P., and Commenges D. A penalized likelihood approach for a progressive three-state model with censored and truncated data: Application to AIDS. Biometrics 55 (1999) 887-890
11. Joly P., Commenges D., and Letenneur L. A penalized likelihood approach for arbitrarily censored and truncated data: Application to age-specific incidence of dementia. Biometrics 54 (1998) 185-194
12. Joly P., Letenneur L., Alioum A., and Commenges D. PHMPL: A computer program for hazard estimation using a penalized likelihood method with interval-censored and left-truncated data. Computer Methods and Programs in Biomedicine 60 3 (1999) 225-231
13. Laird N., and Ware J. Random-effects models for longitudinal data. Biometrics 38 (1982) 963-974
14. Letenneur L., Commenges D., Dartigues J.F., and Barberger-Gateau P. Incidence of dementia and Alzheimer's disease in elderly community residents of south-western France. International Journal of Epidemiology 23 6 (1994) 1256-1261
15. Lin H., McCulloch C.E., and Rosenheck R.A. Latent pattern mixture models for informative intermittent missing data in longitudinal studies. Biometrics 60 2 (2004) 295-305
16. Lin H., McCulloch C.E., Turnbull B.W., Slate E.H., and Clark L.C. A latent class mixed model for analysing biomarker trajectories with irregularly scheduled observations. Statistics in Medicine 19 10 (2000) 1303-1318
17. Lin H., Turnbull B.W., McCulloch C.E., and Slate E.H. 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 (2002) 53-65
18. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11 (1963) 431-441
19. Miloslavsky M., and van der Laan M.J. Fitting of mixtures with unspecified number of components using cross validation distance estimate. Computational Statistics and Data Analysis 41 3-4 (2003) 413-428
20. Muthén B., and Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55 2 (1999) 463-469
21. O'Sullivan F. Fast computation of fully automated log-density and log-hazard estimators. SIAM Journal on Scientific and Statistical Computing 9 (1988) 363-379
22. Pepe M.S., and Janes H. Insights into latent class analysis of diagnostic test performance. Biostatistics 8 2 (2007) 474-484
23. Proust C., Jacqmin-Gadda H., Taylor J.M., Ganiayre J., and Commenges D. A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data. Biometrics 62 4 (2006) 1014-1024
24. Proust-Lima C., Letenneur L., and Jacqmin-Gadda H. A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Statistics in Medicine 26 (2007) 2229-2245
25. Ramsay J. Monotone regression splines in action. Statistical Science 3 4 (1988) 425-461
26. Roy J., and Lin X. Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 56 (2000) 1047-1054
27. Schwartz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
28. Wechsler D. WAIS-R Manual (1981), Psychological Corporation, New York