Informed Bayesian survival analysis
融入先验信息的贝叶斯生存分析AI 解读 ↓
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融入先验信息的贝叶斯生存分析
- 研究问题
- 在参数生存分析中，贝叶斯估计、检验和模型平均如何利用历史信息与模型不确定性，并影响固定样本及序贯试验的决策？
- 方法与思路
- 作者概述贝叶斯生存分析，以结肠癌试验为例，并通过固定样本和序贯模拟，与采用 AIC 或 BIC 选模的频率学方法比较。
- 材料陈述的贡献
- 摘要报告序贯设计中决策时间缩短，并指出模型误设时假阴性和未决试验增加，以及固定样本下功效、误差和估计精度的权衡。
- 阅读关注点
- 应核对历史信息先验、模型平均权重、停止规则及证据阈值，并检查误设情形、错误率口径和决策时间比较的设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要中的时间和误差表现来自特定示例与模拟，不能外推至所有临床试验。
原文摘要
We overview Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. We illustrate the application of the Bayesian approaches on an example data set from a colon cancer trial. We compare the Bayesian parametric survival analysis and frequentist models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. In the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect on disease-free survival in patients with resected colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We have made the analytic approach readily available in RoBSA R package. The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of greatly shortening the length of clinical trials, and provides a richer set of inferences.