Biometrika Advance Access originally published online on January 31, 2008
Biometrika 2008 95(1):63-74; doi:10.1093/biomet/asm087
Articles |
Shared parameter models under random effects misspecification
Biostatistical Centre, Catholic University of Leuven, Kapucijnenvoer 35, B–3000 Leuven, Belgium dimitris.rizopoulos{at}med.kuleuven.be geert.verbeke{at}med.kuleuven.be
Center for Statistics, Hasselt University, Agoralaan 1, B–3590 Diepenbeek, Belgium geert.molenberghs{at}uhasselt.be
Received for publication 1 June 2006. Revision received 1 June 2007.
A common objective in longitudinal studies is the investigation of the association structure between a longitudinal response process and the time to an event of interest. An attractive paradigm for the joint modelling of longitudinal and survival processes is the shared parameter framework, where a set of random effects is assumed to induce their interdependence. In this work, we propose an alternative parameterization for shared parameter models and investigate the effect of misspecifying the random effects distribution in the parameter estimates and their standard errors.
Key Words: Copula function • Dropout • Joint modelling • Sandwich variance estimator
References
-
Cox D., Hinkley D. Theoretical Statistics (1974) London: Chapman and Hall.
Follmann D., Wu M. An approximate generalized linear model with random effects for informative missing data. Biometrics (1995) 51:151–68.[CrossRef][Web of Science][Medline]
Henderson R., Diggle P., Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics (2000) 1:465–80.[Abstract]
Heyde C., Johnstone I. On asymptotic posterior normality for stochastic processes. J. R. Statist. Soc. B (1979) 41:184–9.
Holland P., Wang Y.-J. Dependence function for continuous bivariate densities. Commun. Statist. A (1987) 16:863–76.
Huang X., Stefanski L., Davidian M. Latent-model robustness in structural measurement error models. Biometrika (2006) 93:53–64.
Jones M. The local dependence function. Biometrika (1996) 83:899–904.
Kalbfleisch J., Prentice R. The Statistical Analysis of Failure Time Data (2002) 2nd ed. New York: Wiley.
Keiding N., Andersen P. K., Klein J. The role of frailty models and accelerated failure time models in describing heterogeneity due to omitted covariates. Statist. Med. (1997) 16:215–24.[CrossRef]
Nelsen R. An Introduction to Copulas (1999) New York: Springer-Verlag.
R Development Core Team. R: A Language and Environment for Statistical Computing (2006) Vienna, Austria: R Foundation for Statistical Computing.
Scharfstein D., Rotnitzky A., Robins J. Adjusting for nonignorable dropout using semiparametric non-response models (with Discussion). J. Am. Statist. Assoc. (1999) 94:1096–120.[CrossRef][Web of Science]
Song X., Davidian M., Tsiatis A. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics (2002) 58:742–53.[CrossRef][Web of Science][Medline]
Tsiatis A., Davidian M. A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika (2001) 88:447–58.
Tsiatis A., Davidian M. An overview of joint modeling of longitudinal and time-to-event data. Statist. Sinica (2004) 14:793–818.
Wang Y., Taylor J. Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. J. Am. Statist. Assoc. (2001) 96:895–905.[CrossRef][Web of Science]
White H. Maximum likelihood estimation of misspecified models. Econometrica (1982) 50:1–26.[CrossRef][Web of Science]
Wu M., Carroll R. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics (1988) 44:175–88.[CrossRef][Web of Science]
Wulfsohn M., Tsiatis A. A joint model for survival and longitudinal data measured with error. Biometrics (1997) 53:330–9.[CrossRef][Web of Science][Medline]
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