Abstract
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.
Highlights
Transplantation is often the only viable treatment for children with end-stage liver disease [1], but the shortage of donor livers means that not every child on the waiting list can receive a transplant
Since 2002, prioritization on the waiting list is determined by the model for end-stage liver disease (MELD)/pediatric end-stage liver disease (PELD) severity score, which allocates organs to the sickest individuals first [2]
Among children transplanted during the MELD/PELD era, disease-specific Kaplan-Meier survival plots indicate that transplant recipients with cancer had significantly lower posttransplant survival rates than those with other diseases
Summary
Transplantation is often the only viable treatment for children with end-stage liver disease [1], but the shortage of donor livers means that not every child on the waiting list can receive a transplant. Among children transplanted during the MELD/PELD era, disease-specific Kaplan-Meier survival plots indicate that transplant recipients with cancer had significantly lower posttransplant survival rates than those with other diseases (logrank test P < 0.001). We used this subgroup of transplant recipients to compare two alternative methods for estimating posttransplant survival and its significant covariates. Survival models have been developed using Cox proportional Hazards (PH) models [3], but some diseases do not adhere to the Computational and Mathematical Methods in Medicine basic assumption of proportional hazards, implying that the covariate effects are not constant over time. The objective of the paper is to demonstrate that Gray PC-TVC model can provide more flexibility in capturing the temporal dynamics of covariate effects during posttransplant period
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