TY - JOUR
T1 - Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.
AU - Rustand, Denis
AU - Van Niekerk, Janet
AU - Krainski, Elias Teixeira
AU - Rue, Haavard
AU - Proust-Lima, Cécile
N1 - KAUST Repository Item: Exported on 2023-08-05
Acknowledgements: C.P.-L.’s work was supported by the French National Research Agency (ANR) project JMECR (ANR-21-CE36-0013-01).
PY - 2023/8/2
Y1 - 2023/8/2
N2 - Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.
AB - Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.
UR - http://hdl.handle.net/10754/677867
UR - https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxad019/7235825
U2 - 10.1093/biostatistics/kxad019
DO - 10.1093/biostatistics/kxad019
M3 - Article
C2 - 37531620
SN - 1465-4644
JO - Biostatistics (Oxford, England)
JF - Biostatistics (Oxford, England)
ER -