Advanced SearchSearch Tips
Joint model of longitudinal data with informative observation time and competing risk
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Joint model of longitudinal data with informative observation time and competing risk
Kim, Yang-Jin;
  PDF(new window)
Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients` data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.
competing risk;Drop out;informative observation process;joint model;longitudinal data;random effect;
 Cited by
Cook, R. and Lawless, J. (2007). The Statistical Analysis of Recurrent Events, Springer.

Diggle, P. J., Heagerty, P., Liang, K.-Y., and Zeger, S. L. (2001). Analysis of Longitudinal Data, Oxford Press.

Elashoff, R. M., Li, G., and Li, N. (2007). An approach to joint analysis of longitudinal measurements and competing risks failure time data, Statistics in Medicine, 26, 2813-2835. crossref(new window)

Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 94, 496-509. crossref(new window)

Fitzmaurice, G. M., Laird, N. M., and Rotnitzky, A. G. (2004). Applied Longitudinal Analysis, Wiley, Hoboken NJ.

Free, E. (2004). Longitudinal and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press.

Goldstein, H. (1995). Multilevel Statistical Models, 2nd Ed, Edward Arnold, London.

Harville, D. (1977). Maximum likelihood approaches to variance component estimation and to related problems, Journal of the American Statistical Association, 72, 320-338. crossref(new window)

Henderson, R., Diggle, P., and Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data, Biostatistics, 1, 465-480. crossref(new window)

Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data, John Wiley, New York.

Kim, Y.-J. (2010). Statistical Analysis of recidivism data using frailty effect, Korean Journal of Applied Statistics, 23, 715-724. crossref(new window)

Laird, N. M. and Ware, J. H. (1982). Random effects models for longitudinal data, Biometrics, 38, 963-974. crossref(new window)

Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models, Biometrika, 73, 13-22. crossref(new window)

Liu, L. and Huang, X. (2009). Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome, Applied Statistics, 58, 65-81.

Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: with Applications in R, Chapman and Hall/CRC.

Sun, J., Sun, D., and Liu, D. (2007). Regression analysis of longitudinal data in the presence of informative observation and censoring times, Journal of the American Statistical Association, 102, 1397-1406. crossref(new window)

Tsiatis, A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: an overview, Statistica Sinica, 14, 809-834.

Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models, and Gauss-Newton method, Biometrika, 61, 439-447.

Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error, Biometrics, 53, 330-339. crossref(new window)