Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model
结构化 logistic–normal 混合模型的亚组分析推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以识别治疗效应增强的亚组及检验治疗异质性为目标。
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结构化 logistic–normal 混合模型的亚组分析推断
- 研究问题
- 如何检验是否存在治疗效应更强的潜在亚组，同时识别能够预测亚组归属的变量，而非仅给出探索性的分组结果？
- 方法与思路
- 作者构建结构化 logistic–normal 混合模型，结合混合模型假设检验文献与 bootstrap 近似，检验亚组存在并构造归属评分。
- 材料陈述的贡献
- 摘要称，该模型结合确认性检验和亚组预测，检验具有较好功效且对调参不敏感，并以 AIDS 临床试验数据展示用途。
- 阅读关注点
- 应核对亚组与治疗效应的模型定义、亚组不存在时的检验校准，以及预测评分、调参敏感性和治疗异质性解释的依据。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；亚组识别条件和数值结果未说明，应用中的潜在亚组不能直接视为经外部验证的结论。
原文摘要
In this article, we propose a statistical model for the purpose of identifying a subgroup that has an enhanced treatment effect as well as the variables that are predictive of the subgroup membership. The need for such subgroup identification arises in clinical trials and in market segmentation analysis. By using a structured logistic-normal mixture model, our proposed framework enables us to perform a confirmatory statistical test for the existence of subgroups, and at the same time, to construct predictive scores for the subgroup membership. The inferential procedure proposed in the article is built on the recent literature on hypothesis testing for Gaussian mixtures, but the structured logistic-normal mixture model enjoys some distinctive properties that are unavailable to the simpler Gaussian mixture models. With the bootstrap approximations, the proposed tests are shown to be powerful and, equally importantly, insensitive to the choice of tuning parameters. As an illustration, we analyze a dataset from the AIDS Clinical Trials Group 320 study and show how the proposed methodology can help detect a potential subgroup of AIDS patients who may react much more favorably to the addition of a protease inhibitor to a conventional regimen than other patients.