将治疗反应作为潜变量
Treatment response as a latent variable
AI总结：
针对区分治疗反应者与非反应者的统计推断问题，提出因果两组（C2G）模型及半参数、完全非参数两种经验贝叶斯方法，控制错误发现率且具近最优功效，在癌症免疫治疗数据集上恢复临床验证的预测生物标志物。
摘要：
科学家们常常需要分析研究中对治疗有反应的样本，以完善假设并寻找反应的潜在因果驱动因素。结果的自然变异使得区分反应者与非反应者成为一个统计推断问题。为处理潜反应，我们引入因果两组（C2G）模型，这是经典两组模型的因果扩展。C2G模型假设，根据某种先验概率，接受治疗的样本可能会或可能不会经历效果。我们为因果两组模型提出两种经验贝叶斯方法，一种在半参数条件下，另一种在完全非参数条件下。半参数模型假设加性治疗效果，可从观测数据中识别。非参数模型不可识别，但我们证明它仍可用于测试每个治疗样本的反应。我们通过实证和理论证明，两种选择反应者的方法能将错误发现率控制在目标水平，且具有近最优功效。我们还提出两种新的感兴趣估计量，并提供在不可识别非参数模型中推导估计量区间的策略。在癌症免疫治疗数据集上，非参数C2G模型恢复了临床验证的阳性和阴性结果的预测生物标志物。代码可在https://github.com/tansey-lab/causal2groups获取。
英文摘要：
Scientists often need to analyze the samples in a study that responded to treatment in order to refine their hypotheses and find potential causal drivers of response. Natural variation in outcomes makes teasing apart responders from non-responders a statistical inference problem. To handle latent responses, we introduce the causal two-groups (C2G) model, a causal extension of the classical two-groups model. The C2G model posits that treated samples may or may not experience an effect, according to some prior probability. We propose two empirical Bayes procedures for the causal two-groups model, one under semi-parametric conditions and another under fully nonparametric conditions. The semi-parametric model assumes additive treatment effects and is identifiable from observed data. The nonparametric model is unidentifiable, but we show it can still be used to test for response in each treated sample. We show empirically and theoretically that both methods for selecting responders control the false discovery rate at the target level with near-optimal power. We also propose two novel estimands of interest and provide a strategy for deriving estimand intervals in the unidentifiable nonparametric model. On a cancer immunotherapy dataset, the nonparametric C2G model recovers clinically-validated predictive biomarkers of both positive and negative outcomes. Code is available at https://github.com/tansey-lab/causal2groups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
