A Bayesian promotion time cure rate model with current status data

In medical studies of treatable fatal diseases, some individuals may become cured, causing survival curves to plateau instead of declining to zero. Cure rate models estimate the cure fraction, the distribution of susceptible individuals, and covariate effects. To study the data with a cure fraction, the promotion time cure rate model is favored, owing to its biological interpretability in cancer metastasis. Epidemiological and destructive testing data are often subject to current status censoring, where event status is recorded only once. While the promotion time cure rate model has been extensively studied under various censoring schemes, its Bayesian formulation for current status data remains largely unexplored. Motivated by this, we develop a Bayesian promotion time cure rate model specifically for current status data. The posterior computation is carried out via an adaptive Metropolis-Hastings algorithm. Simulation studies prove our approach’s efficiency, while analyses of lung tumor and breast cancer data illustrate its utility. By integrating prior knowledge with data, this method enhances understanding of disease dynamics and aids in developing strategies to improve cure rates.