Bayesian multivariate logistic regression for superiority and inferiority decision-making under observable treatment heterogeneity
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可观测治疗异质性下优效与劣效决策的贝叶斯多元逻辑回归
- 研究问题
- 在具有多元二元结局和可观测治疗异质性的随机试验中，如何估计总体及亚组效应，并按预设错误率进行治疗比较？
- 方法与思路
- 作者结合 Pólya–Gamma 展开的贝叶斯多元逻辑回归、概率尺度转换与决策程序，并提供非信息先验下的样本量估算。
- 材料陈述的贡献
- 摘要称数值评估获得预期决策错误率，样本充分大时效应估计无偏；卒中试验示例呈现平均效应分析可能遗漏的异质性。
- 阅读关注点
- 应核对多元结局依赖的建模、优效与劣效决策规则、先验敏感性，以及总体和亚组错误率与样本量计算的关系。
- 阅读边界
- 这是 arXiv 预印本，不能确认同行评审或期刊接收。摘要未说明充分大样本的标准，应用中的异质性趋势也不等于确定结论。
原文摘要
The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend towards heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.