Bayesian inference for survival prediction of childhood Leukemia. (April 2023)

• Record Type: Journal Article
• Authors: Cui, Yuning; Li, Yifu; Pan, Chongle; Brown, Stephanie R.; Gallant, Rachel E.; Zhu, Rui
• Is Part Of: Computers in biology and medicine. Volume 156(2023)
• Publication Date: 2023-04
• Subjects: Bayesian inference -- Survival analysis -- Childhood Leukemia -- Hamiltonian Monte Carlo -- Model uncertainty; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106713
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Childhood Leukemia is the most common type of cancer among children. Nearly 39% of cancer-induced childhood deaths are attributable to Leukemia. Nevertheless, early intervention has long been underdeveloped. Moreover, there are still a group of children succumbing to their cancer due to the cancer care resource disparity. Therefore, it calls for an accurate predictive approach to improve childhood Leukemia survival and mitigate these disparities. Existing survival predictions rely on a single best model, which fails to consider model uncertainties in predictions. Prediction from a single model is brittle, with model uncertainty neglected, and inaccurate prediction could lead to serious ethical and economic consequences. Methods: To address these challenges, we develop a Bayesian survival model to predict patient-specific survivals by taking model uncertainty into account. Specifically, we first develop a survival model predict time-varying survival probabilities. Second, we place different prior distributions over various model parameters and estimate their posterior distribution with full Bayesian inference. Third, we predict the patient-specific survival probabilities changing with respect to time by considering model uncertainty induced by posterior distribution. Results: Concordance index of the proposed model is 0.93. Moreover, the standardized survival probability of the censored group is higher than that of the deceased group. Conclusions: Experimental results indicate that the proposed model is robust and accurate in predicting patient-specific survivals. It can also help clinicians track the contribution of multiple clinical attributes, thereby enabling well-informed intervention and timely medical care for childhood Leukemia. Highlights: A robust Bayesian survival model for patient-specific survival probability predictions of childhood Leukemia. Model uncertainty capturing based on a full Bayesian inference approach. Clinical attributes effects tracking for better decision-making on interventions.