Using time-varying models to estimate post-transplant survival in pediatric liver transplant recipients

• Published in: PloS one 2018-05, Vol.13 (5), p.e0198132-e0198132
• Main Authors: Bryce, Cindy L, Chang, Chung Chou H, Ren, Yi, Yabes, Jonathan, Zenarosa, Gabriel, Iyer, Aditya, Tomko, Heather, Squires, Robert H, Roberts, Mark S
• Format: Article
• Language: English
• Subjects: Age; Biology and Life Sciences; Child; Children; Creatinine; Demographics; Diagnosis; Encephalopathy; Engineering and Technology; Engineering schools; Female; Gastroenterology; Graft Survival; Grafting; Hazards; Health aspects; Humans; Identification methods; Industrial engineering; Kidneys; Liver; Liver cirrhosis; Liver diseases; Liver transplantation; Liver Transplantation - adverse effects; Liver transplants; Male; Medicine; Medicine and Health Sciences; Minority & ethnic groups; Models, Statistical; Multivariate Analysis; Nutrition; Pediatrics; Prognosis; Public health; Quality of life; Simulation; Survival; Survival Analysis; Time Factors; Transplants & implants
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0198132

To distinguish clinical factors that have time-varying (as opposed to constant) impact upon patient and graft survival among pediatric liver transplant recipients. Using national data from 2002 through 2013, we examined potential clinical and demographic covariates using Gray's piecewise constant time-varying coefficients (TVC) models. For both patient and graft survival, we estimated univariable and multivariable Gray's TVC, retaining significant covariates based on backward selection. We then estimated the same specification using traditional Cox proportional hazards (PH) models and compared our findings. For patient survival, covariates included recipient diagnosis, age, race/ethnicity, ventilator support, encephalopathy, creatinine levels, use of living donor, and donor age. Only the effects of recipient diagnosis and donor age were constant; effects of other covariates varied over time. We retained identical covariates in the graft survival model but found several differences in their impact. The flexibility afforded by Gray's TVC estimation methods identify several covariates that do not satisfy constant proportionality assumptions of the Cox PH model. Incorporating better survival estimates is critical for improving risk prediction tools used by the transplant community to inform organ allocation decisions.