基于当前状态数据的贝叶斯促进时间治愈模型
A Bayesian promotion time cure model with current status data
AI总结：
本研究首次提出基于当前状态数据的贝叶斯促进时间治愈模型，采用自适应Metropolis-Hastings算法进行后验计算，通过模拟与癌症数据分析证明其有效性，有望结合先验知识提升临床治愈率。
摘要：
分析来自流行病学研究或破坏性测试的寿命数据通常涉及当前状态删失，即个体仅被检查一次，其事件状态仅在该特定时间点被记录。在实际中，部分个体可能永远不会经历感兴趣的事件，从而导致带有治愈比例的当前状态数据。治愈模型用于估计非易感个体的比例、易感个体的分布以及协变量效应。在癌症转移生物学解释的启发下，promotion time cure model 成为分析此类数据时 mixture cure rate model 的一种流行替代方法。本研究首次提出了一种贝叶斯推断程序，采用 promotion time cure model 分析带有治愈比例的当前状态数据。该方法利用自适应 Metropolis-Hastings 算法进行后验计算。模拟研究证明了该方法的有效性，而肺癌肿瘤和乳腺癌数据的分析则展示了其实用价值。该方法通过整合有关疾病动态和治疗方案的先验知识，有望提高临床治愈率。
英文摘要：
Analysis of lifetime data from epidemiological studies or destructive testing often involves current status censoring, wherein individuals are examined only once and their event status is recorded only at that specific time point. In practice, some of these individuals may never experience the event of interest, leading to current status data with a cured fraction. Cure models are used to estimate the proportion of non-susceptible individuals, the distribution of susceptible ones, and covariate effects. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analysing such data. The current study is the first to put forth a Bayesian inference procedure for analysing current status data with a cure fraction, resorting to a promotion time cure model. An adaptive Metropolis-Hastings algorithm is utilised for posterior computation. Simulation studies prove our approach's efficiency, while analyses of lung tumor and breast cancer data illustrate its practical utility. This approach has the potential to improve clinical cure rates through the incorporation of prior knowledge regarding the disease dynamics and therapeutic options.
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