Score test for conditional independence between longitudinal outcome and time to event given the classes in the joint latent class model

Biometrics

Volumn 66, Issue 1, 2010, Pages 11-19

  Jacqmin Gadda, Hélène ^a,b   Proust Lima, Cécile ^a,b   Taylor, Jeremy M G ^c   Commenges, Daniel ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE BORDEAUX   (France)

^c UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Joint model; Latent class model; Mixture model; Model diagnosis

Indexed keywords

ANTIGENS; DIAGNOSIS; DISEASES; PATIENT MONITORING; RANDOM PROCESSES; RISK PERCEPTION; STATISTICAL TESTS; UROLOGY;

CONDITIONAL INDEPENDENCES; JOINT MODELS; LATENT CLASS; LATENT CLASS MODEL; MIXED MODELING; MIXTURE MODELING; MODEL DIAGNOSIS; RISK FUNCTION; SCORE TEST; TIME TO EVENTS;

RADIOTHERAPY;

CANCER; EVENT STRATIGRAPHY; EVOLUTIONARY BIOLOGY; GENETIC MARKER; HYPOTHESIS TESTING; MATHEMATICAL ANALYSIS; NUMERICAL MODEL; POPULATION MODELING; POPULATION STRUCTURE; QUANTITATIVE ANALYSIS; RECURRENCE INTERVAL; TIME SERIES;

ALGORITHM; BIOMETRY; COMPUTER SIMULATION; HUMAN; LONGITUDINAL STUDY; OUTCOME ASSESSMENT; PROCEDURES; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BIOMETRY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE);

EID: 77949704277     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01234.x     Document Type: Article

References (18)

1. Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L., and Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association 92, 1375-1386.
2. Beunckens, C., Molenberghs, G., Verbeke, G., and Mallinckrodt, C. (2008). A latent-class mixture model for incomplete longitudinal Gaussian data. Biometrics 64, 96-105.
3. Commenges, D., Letenneur, L., Jacqmin, H., Moreau, T., and Dartigues, J. F. (1994). Test of homogeneity of binary data with explanatory variables. Biometrics 50, 613-620.
4. Dantan, E., Proust-Lima, C., Letenneur, L., and Jacqmin-Gadda, H. (2008). Pattern mixture models and latent class models for the analysis of multivariate longitudinal data with informative dropout. International Journal of Biostatistics 4, art 14. Available at: http://www.bepress.com/ijb/ vol4/iss1/14.
5. Diggle, P. and Kenward, M. (1994). Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society Series C, Applied Statistics 43, 49-93.
6. Freedman, D. A. (2007). How can the score test be inconsistent? The American Statistician 61, 291-295.
7. Guo, J., Wall, M., and Amemyia, Y. (2006). Latent class regression on latent factors. Biostatistics 7, 145-163.
8. Henderson, R., Diggle, P., and Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics 1, 465-480.
9. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963-974.
10. Lin, H., Turnbull, B. W., McCulloch, C. E., 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.
11. Lin, H., McCulloch, C. E., and Rosenheck, R. A. (2004). Latent pattern mixture models for informative intermittent missing data in longitudinal studies. Biometrics 60, 295-305.
12. Proust-Lima, C. and Taylor, J. M. G. (2009). Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures post-treatment PSA. To appear in Biostatistics.
13. Proust-Lima, C., Taylor, J. M. G., Williams, S. G., Ankerst, D. P., Liu, N., Kestin, L. L., Bae, K., and Sandler, H. M. (2008). Determinants of change of prostate-specific antigen over time and its association with recurrence following external beam radiation therapy of prostate cancer in 5 large cohorts. International Journal of Radiation Oncology, Biology, Physics 72, 782-791.
14. Proust-Lima, C., Joly, P., and Jacqmin-Gadda, H. (2009). Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach. Computational Statistics and Data Analysis 53, 1142-1154.
15. Roy, J. (2003). Modeling longitudinal data with nonignorable dropouts using a latent dropout class model. Biometrics 59, 829-836.
16. Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461-464.
17. Taylor, J. M. G., Yu, M., and Sandler, H. M. (2005). Individualized predictions of disease progression following radiation therapy for prostate cancer. Journal of Clinical Oncology 23, 816-825.
18. 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.