Gray's time-varying coefficients model for posttransplant survival of pediatric liver transplant recipients with a diagnosis of cancer

Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray's piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable (P < 0.15) and multivariable models with forward selection (P < 0.05) for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time. © 2013 Yi Ren et al

A biologically based discrete-event simulation model of liver transplantation in the United States for pediatric and adult patients

We describe the framework of a discrete-event simulation of the national liver allocation system that incorporates the stochastic, disease-specific natural histories of pediatric and adult patients independent of allocation policies. This model will extend our previous work that only considered adult patients and organs. Our model will consist of patient and organ generators, a natural history progression module, and pre- and post-transplant survival estimation modules. While this is still a work in progress, our model will produce various statistics, such as the number of deaths while waiting for a liver, waitlist additions, the number of transplants performed, the number of wasted livers, and estimates of pre- and post-transplant survival at every time point for every patient. © 2011 IEEE