4.2 Article

Joint analysis of longitudinal measurements and survival times with a cure fraction based on partly linear mixed and semiparametric cure models

Journal

PHARMACEUTICAL STATISTICS
Volume 20, Issue 2, Pages 362-374

Publisher

WILEY
DOI: 10.1002/pst.2082

Keywords

B‐ spline; censored time; EM algorithm; gamma frailty; gauss quadrature

Funding

  1. Dalian High Level Talent Innovation Programme [2015R051]
  2. Fundamental Research Funds for the Central Universities of China
  3. National Natural Science Foundation of China [11601060]
  4. Natural Sciences and Engineering Research Council of Canada

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A joint model was proposed for analyzing quality of life scores and survival times in a clinical trial on early breast cancer, featuring a mixed effect model and a semiparametric mixture cure model linked by shared random effects. An EM algorithm was used for parameter estimation and performance evaluation was conducted through simulation studies and application to real data.
In a joint analysis of longitudinal quality of life (QoL) scores and relapse-free survival (RFS) times from a clinical trial on early breast cancer conducted by the Canadian Cancer Trials Group, we observed a complicated trajectory of QoL scores and existence of long-term survivors. Motivated by this observation, we proposed in this paper a flexible joint model for the longitudinal measurements and survival times. A partly linear mixed effect model is used to capture the complicated but smooth trajectory of longitudinal measurements and approximated by B-splines and a semiparametric mixture cure model with the B-spline baseline hazard to model survival times with a cure fraction. These two models are linked by shared random effects to explore the dependence between longitudinal measurements and survival times. A semiparametric inference procedure with an EM algorithm is proposed to estimate the parameters in the joint model. The performance of proposed procedures are evaluated by simulation studies and through the application to the analysis of data from the clinical trial which motivated this research.

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