Paper "Modelling longitudinal and time-to-event data: a phase IV simulation study comparing R package implementations of joint models with time-varying Cox proportional-hazards regression, and the two-stage approach" by Heege, Greven, Schaeffner, Grittner and Agner accepted by BMC Medical Research Methodology
The paper Modelling longitudinal and time-to-event data: a phase IV simulation study comparing R package implementations of joint models with time-varying Cox proportional-hazards regression, and the two-stage approach by Jil Heege, Sonja Greven, Elke Schaeffner, Ulrike Grittner and Annette Aigner accepted by BMC Medical Research Methodology.
Abstract:
Joint models offer an unbiased statistical approach for analyzing the effects of longitudinal biomarkers on time-to-event outcomes, providing an alternative to time-varying Cox proportional-hazards regression and the two-stage approach. However, whether available R package implementations of these methods perform reliably across different practically relevant scenarios remains insufficiently studied.
We conducted a simulation study based on the Berlin Initiative Study examining kidney function and survival in older adults. In a manner comparable to phase IV studies in clinical research, our evaluation aims to provide insights into the practical performance of commonly used R package implementations of these methods, mostly under their default settings.
By varying data generating scenarios, we assessed how different numbers of events and longitudinal measurements affect performance of Bayesian (R package JMbayes2) and frequentist joint models (R packages JM and joineRML), time-varying Cox regression (R package survival), and the two-stage approach (R packages nlme and survival), focusing on bias in parameter estimates.
Results revealed substantial variability across implementations. The JM package exhibited considerable bias and frequent convergence issues. In contrast, joineRML performed robustly with approximately unbiased estimates for association parameters and high convergence frequencies comparable to simpler implementations across diverse scenarios.
However, both frequentist packages systematically underestimated the effects of baseline covariates in the survival model. The Bayesian JMbayes2 was largely unbiased, but performance deteriorated under two conditions: with few events (<70), convergence was low and bias persisted even in converged models; and with observation-to-event ratios below 2, convergence declined, although estimates from converged models remained approximately unbiased.
Time-varying Cox regression and the two-stage approach showed more bias than JMbayes2 in certain settings but tended to achieve more robust performance and convergence across most scenarios.