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

Tópicos

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Resumo

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.

Resumo Limpo

transplant often viabl treatment pediatr patient endstag liver diseas make wellinform decis proceed transplant requir accur predictor transplant surviv standard cox proport hazard ph model assum covari effect timeinvari rightcensor failur time howev assumpt may alway hold gray piecewis constant timevari coeffici pctvc model offer greater flexibl captur tempor chang covari effect without lose mathemat simplic cox ph model present work examin cox ph gray pctvc model posttranspl surviv analysi pediatr liver transplant patient diagnos cancer obtain potenti predictor univari p multivari model forward select p cox ph gray pctvc model coincid cox ph model provid reason averag result estim covari effect posttranspl surviv gray model use piecewis constant penal spline show detail effect chang time

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