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Joint Modelling of Survival and Longitudinal Data with Informative Observation Times

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  • Hongsheng Dai
  • Jianxin Pan

Abstract

In this paper, we consider the joint modelling of survival and longitudinal data with informative observation time points. The survival model and the longitudinal model are linked via random effects, for which no distribution assumption is required under our estimation approach. The estimator is shown to be consistent and asymptotically normal. The proposed estimator and its estimated covariance matrix can be easily calculated. Simulation studies and an application to a primary biliary cirrhosis study are also provided.

Suggested Citation

  • Hongsheng Dai & Jianxin Pan, 2018. "Joint Modelling of Survival and Longitudinal Data with Informative Observation Times," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 571-589, September.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:3:p:571-589
    DOI: 10.1111/sjos.12314
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    Cited by:

    1. Chuoxin Ma & Jianxin Pan, 2022. "Multistate analysis of multitype recurrent event and failure time data with event feedbacks in biomarkers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 864-885, June.
    2. Murray, James & Philipson, Pete, 2022. "A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).

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