Bayesian survival time prediction of French Childhood Cancer Survivors’ Study using re-parameterized mixture distribution

In the last few decades, survival rates after childhood cancer have considerably in- creased due to recent treatment technique advances in developed countries. However, even though this progress leads to a growing number of long-term survivors, treatments can damage healthy tis- sues. Consequently, childhood cancer survivors (CCS) carry a significant risk of cancer treatments related to late effects. One of the leading late effects of childhood cancer radiotherapy treatment is cardiac pathology that can lead to mortality and Valvular Heart Disorder (VHD). Early di- agnosis can prove lifesaving, so it is essential to identify early the patients who are at high risk of experiencing at least one cardiac disease to improve therapeutic and follow-up protocols. This study focuses on French Childhood Cancer Survivors Study (FCCSS) patients who had received radiotherapy and chemotherapy. The main objectives of this study are the statistical modeling of the survival time of (FCCSS) patients using mixture distribution and establishing a Bayesian inference to identify the model parameters, predict the survival time, and then determine the risk of cardiac disease. Regarding the model’s statistical inference, we have developed an adaptive Metropolis-within-Gibbs algorithm to estimate the model parameters.