Bayesian Nonparametric Survival Analysis
贝叶斯非参数生存分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要聚焦医学右删失数据的生存预测，未提供所列经济学细分方向的直接依据。
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贝叶斯非参数生存分析
- 研究问题
- 在生存时间存在右删失时，如何利用接受相同治疗、患相同疾病的既有患者数据，预测一个或多个新患者的生存概率？
- 方法与思路
- 作者在生存时间可交换、无删失无并列时区间等概率及非信息删失假设下，扩展百分位数后验以构造生存预测概率。
- 材料陈述的贡献
- 摘要称，给出预测概率的一般表示与计算算法，并针对删失信息近似提供精确信息下的上下界，讨论实现及与其他方法的比较。
- 阅读关注点
- 应核对三项假设的含义、删失信息被如何近似、上下界的计算方式，以及与 Kaplan–Meier 方法比较时预测目标是否一致。
- 阅读边界
- 仅据版本未注明的索引摘要，不能判断删失近似在具体数据中的误差大小；生存预测也不自动构成不同治疗的因果比较。
原文摘要
This article considers a Bayesian nonparametric approach to a (right) censored data problem. Although the results are applicable to a wide variety of such problems, including reliability analysis, the discussion centers on medical survival studies. We extend the posterior distribution of percentiles given by Hill (1968) to obtain predictive posterior probabilities for the survival of one or more new patients, using data from other individuals having the same disease and given the same treatment. The analysis hinges on three assumptions: (a) The new patients and the previous sample patients are all deemed to be exchangeable with regard to survival time. (b) The posterior prediction rule, in the case of no censoring or ties among (say n) observed survival times, assigns equal probability of 1/(n + 1) to each of the n + 1 open intervals determined by these values. (c) The censoring mechanisms are “noninformative.” Detailed discussion of these assumptions is presented from a Bayesian point of view. In addition to obtaining a general representation for the predictive probabilities, intuition-building algorithms for the calculations are discussed. The derivation of these probabilities involves the use of an approximation with regard to the available censoring information. We present upper and lower bounds for the predictive probabilities under the exact censoring information. Substantial discussion is devoted to the practical implementation of the results. Relationships with other Bayesian approaches are discussed, as well as comparisons with the product limit rule of Kaplan and Meier (1958). To enhance the value of these comparisons, we analyze some examples of Kaplan and Meier (1958) and Gehan (1965).