A Latent Promotion Time Cure Rate Model using Dependent Tail-Free Mixtures
该文扩展了潜在促进时间治愈率标记模型，提出用线性依赖尾部分割混合过程作为治愈率参数的先验分布，可回答特定人群高治愈率（如超过70%）的概率问题，适用于右删失生存数据。
Summary The paper extends the latent promotion time cure rate marker model of Kim, Xi and Chen for right-censored survival data. Instead of modelling the cure rate parameter as a deterministic function of risk factors, they assumed that the cure rate parameter of a targeted population is distributed over a number of ordinal levels according to the probabilities governed by the risk factors. We propose to use a mixture of linear dependent tail-free processes as the prior for the distribution of the cure rate parameter, resulting in a latent promotion time cure rate model. This approach provides an immediate answer to perhaps one of the most pressing questions ‘what is the probability that a targeted population has high proportions (e.g. greater than 70%) of being cured?’. The approach proposed can accommodate a rich class of distributions for the cure rate parameter, while centred at gamma densities. The algorithms that are developed in this work allow the fitting of latent promotion time cure rate models with several survival models for metastatic tumour cells.