对比用于临床试验贝叶斯分析的计算算法
Ziming Chen1, Jeffrey S Berger2, Lana A Castellucci3
1The Hospital for Sick Children, Toronto, ON, Canada.
Clinical trials (London, England)
|May 16, 2024
概括
集成嵌套拉普拉斯近似 (INLA) 为贝叶斯临床试验分析提供了马尔科夫链蒙特卡洛 (MCMC) 的更快,更简单的替代方案. 虽然INLA提供了准确的治疗效果估计,但需要进一步的研究来提高其对等级差异估计的准确性.
科学领域:
- * 临床研究中的贝叶斯统计方法.
- * 统计建模中的计算效率.
- *对近似算法的比较分析.
背景情况:
- *贝叶斯方法在临床试验设计和分析方面越来越多地被采用.
- *马尔科夫链蒙特卡洛 (MCMC) 方法通常用于贝叶斯推理,但在计算上是密集和复杂的.
- * 集成嵌套拉普拉斯近似 (INLA) 为MCMC提供了一个计算效率高的替代方案.
研究的目的:
- * 评估INLA在临床试验中的可行性和准确性,作为MCMC的替代方案.
- * 为在贝叶斯临床试验设计中使用INLA提供实用指导.
- *使用真实临床试验数据,将INLA与MCMC算法 (JAGS和stan) 的性能进行比较.
主要方法:
- *使用来自COVID-19临床试验的数据来拟合贝叶斯分层通用混合模型.
- *INLA与在R.实施的两个MCMC算法 (JAGS和stan) 进行了比较.
- *分析了七种不同的结果,包括顺序,二进制和时间到事件数据.
- *记录了后部分布近似的计算时间和准确性.
主要成果:
- *INLA显示了与治疗和性别效应的斯坦的高度重叠 (96-97.6%),表明几乎相同的近似值.
- *INLA比MCMC方法快得多,性能比Stan高出85-269倍,比JAGS高出26-1852倍.
- *虽然INLA对等级效应差异的估计在MCMC可信区间内,但重叠率较低 (77-91.3%).
- *INLA和stan in R的实现很简单,而JAGS需要直接的模型规范.
结论:
- *INLA为贝叶斯临床试验分析提供了一个计算效率高,准确且易于实施的MCMC替代方案.
- *与MCMC方法相比,INLA显著降低了计算复杂性和时间.
- *建议进行进一步的研究,以提高INLA在估计等级效应变异的准确性,特别是在比例赔率模型中.
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