Jackson Holly, Shou Yiyun, Azad Nur Amira Binte Mohamed, Chua Jing Wen, Perez Rebecca Lynn, Wang Xinru, de Kraker Marlieke E A, Mo Yin
Infection Control Program, Geneva University Hospitals and Faculty of Medicine, World Health Organization Collaborating Center, Geneva, Switzerland.
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
BMC Med Res Methodol. 2025 May 29;25(1):149. doi: 10.1186/s12874-025-02537-x.
Multiple treatment options frequently exist for a single medical condition with no single standard of care (SoC), rendering a classic randomised trial comparing a specific treatment to a control treatment infeasible. A novel design, the personalised randomised controlled trial (PRACTical), allows individualised randomisation lists and borrows information across patient subpopulations to rank treatments against each other without comparison to a SoC. We evaluated standard frequentist analysis with Bayesian analyses, and developed a novel performance measure, utilising the precision in treatment coefficient estimates, for treatment ranking.
We simulated trial data to compare four targeted antibiotic treatments for multidrug resistant bloodstream infections as an example. Four patient subgroups were simulated based on different combinations of patient and bacteria characteristics, which required four different randomisation lists with some overlapping treatments. The primary outcome was binary, using 60-day mortality. Treatment effects were derived using frequentist and Bayesian analytical approaches, with logistic multivariable regression. The performance measures were: probability of predicting the true best treatment, and novel proxy variables for power (probability of interval separation) and type I error (probability of incorrect interval separation). Several scenarios with varying treatment effects and sample sizes were compared.
The Frequentist model and Bayesian model using a strong informative prior, were both likely to predict the true best treatment ( ) and gave a large probability of interval separation (reaching a maximum of ), at a given sample size. Both methods had a low probability of incorrect interval separation ( ), for all sample sizes ( ) in the null scenarios considered. The sample size required for probability of interval separation to reach 80% ( ), was larger than the sample size required for the probability of predicting the true best treatment to reach 80% ( ).
Utilising uncertainty intervals on the treatment coefficient estimates are highly conservative, limiting applicability to large pragmatic trials. Bayesian analysis performed similarly to the frequentist approach in terms of predicting the true best treatment.
对于单一医疗状况，通常存在多种治疗选择，且不存在单一的标准治疗方案（SoC），这使得比较特定治疗与对照治疗的经典随机试验变得不可行。一种新颖的设计，即个性化随机对照试验（PRACTical），允许个性化随机化列表，并跨患者亚组借用信息，以便在不与SoC比较的情况下对治疗进行相互排序。我们用贝叶斯分析评估了标准频率分析，并开发了一种利用治疗系数估计精度的新型性能指标来进行治疗排序。
我们以比较四种针对多重耐药血流感染的靶向抗生素治疗为例，模拟试验数据。根据患者和细菌特征的不同组合模拟了四个患者亚组，这需要四个不同的随机化列表，其中一些治疗有重叠。主要结局为二元结局，采用60天死亡率。使用频率分析和贝叶斯分析方法，通过逻辑多变量回归得出治疗效果。性能指标为：预测真正最佳治疗的概率，以及用于检验效能（区间分离概率）和I型错误（错误区间分离概率）的新型替代变量。比较了几种治疗效果和样本量不同的情况。
在给定样本量下，使用强信息先验的频率模型和贝叶斯模型都有可能预测真正最佳治疗（ ），并给出较大的区间分离概率（最高达到 ）。在考虑的零假设情况下，对于所有样本量（ ），两种方法的错误区间分离概率都较低（ ）。区间分离概率达到80%（ ）所需的样本量大于预测真正最佳治疗概率达到80%（ ）所需的样本量。
利用治疗系数估计的不确定性区间非常保守，限制了其在大型实用试验中的适用性。在预测真正最佳治疗方面，贝叶斯分析与频率分析方法表现相似。
