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JOINT MODELS FOR NONLINEAR LONGITUDINAL AND TIME-TO-EVENT DATA USING PENALISED SPLINES

Part of: Survival analysis and censored data Numerical approximation and computational geometry Parametric inference Statistics

Published online by Cambridge University Press:  07 January 2019

HUONG THI THU PHAM
HUONG THI THU PHAM*
Affiliation:
Department of Mathematics, An Giang University, An Giang Province, Vietnam email ptthuong@agu.edu.vn
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Abstract

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Keywords

survival datalongitudinal datatwo-stage approachshared random effects approachjoint models

MSC classification

Primary: 62-07: Data analysis
Secondary: 62N05: Reliability and life testing 62F15: Bayesian inference 65D07: Splines
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 99 , Issue 2 , April 2019 , pp. 349 - 352
Copyright
© 2019 Australian Mathematical Publishing Association Inc. 

Footnotes

Thesis submitted to Flinders University in July 2017; degree approved on 6 April 2018; principal supervisor Darfiana Nur; co-supervisors Alan Branford and Murk Bottema.

References

Andersen, P. K., Borgan, O., Gill, R. D. and Keiding, N., Statistical Models Based on Counting Processes (Springer, New York, 1993).Google Scholar
Cox, D. R., ‘Regression models and life-tables’, J. R. Stat. Soc. Ser. B Stat. Methodol. 34(2) (1972), 187–220.Google Scholar
Cox, D. R., ‘Partial likelihood’, Biometrika 62(2) (1975), 269–276.Google Scholar
Cox, D. R. and Hinkley, D. V., Theoretical Statistics (Chapman and Hall/CRC Press, New York, 1979).Google Scholar
Cox, D. and Oakes, D., Analysis of Survival Data (Chapman and Hall, London, 1984).Google Scholar
Ding, J. and Wang, J., ‘Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data’, Biometrics 64(2) (2008), 546–556.Google Scholar
Gould, A., Boye, M. E., Crowther, M. J., Ibrahim, J. G., Quartey, G., Micallef, S. and Bois, F. Y., ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group’, Stat. Med. 34(14) (2014), 2181–2195.Google Scholar
Ibrahim, J. G., Chen, M. and Sinha, D., Bayesian Survival Analysis, Wiley Online Library (John Wiley, Hoboken, NJ, 2005).Google Scholar
Kalbfleisch, J. and Prentice, R., The Statistical Analysis of Failure Time Data, 2nd edn (Wiley, New York, 2002).Google Scholar
Rizopoulos, D., ‘Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule’, Comput. Statist. Data Anal. 56 (2011), 491–501.Google Scholar
Rizopoulos, D., Joint Models for Longitudinal and Time-to-Event Data with Applications in R (Chapman and Hall/CRC Press, New York, 2012).Google Scholar
Sweeting, M. J. and Thompson, S. G., ‘Joint modelling of longitudinal and time to event data with application to predicting abdominal aortic aneurysm growth and rupture’, Biom. J. 53(5) (2011), 750–763.Google Scholar
Tsiatis, A. A. and Davidian, M., ‘A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error’, Biometrika 88(2) (2001), 447–458.Google Scholar
Tsiatis, A. A. and Davidian, M., ‘Joint modeling of longitudinal and time-to-event data: an overview’, Statist. Sinica 14 (2004), 809–834.Google Scholar
Tsiatis, A. A., Degruttola, V. and Wulfsohn, M. S., ‘Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS’, J. Amer. Statist. Assoc. 90(429) (1995), 27–37.Google Scholar