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估计纵向因果剂量反应关系的贝叶斯框架AI 解读

Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study outcome, offering limited tools for characterizing marginal causal dose-response relationships under continuous exposures. We propose a scalable, nonparametric Bayesian framework for estimating marginal longitudinal causal dose-response functions with repeated outcome measurements. Our approach…

事件时间结果因果亚组估计的贝叶斯层级自助框架AI 解读

Causal estimation of treatment effects within prespecified subgroups, such as biomarker-defined strata, disease phenotypes, or demographic groups are often of clinical interest. Bayesian approaches are attractive for subgroup effect estimation because flexible priors can represent complex treatment heterogeneity and propagate posterior uncertainty. Frequentist methods for prespecified causal subgroup analysis with time-to-event outcomes are also available, including…

时变未测量混杂下因果估计的贝叶斯敏感性分析AI 解读

Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estimates. Among sensitivity analysis methods proposed in the literature for unmeasured confounding, the latent confounder approach is favoured for its intuitive interpretation via the use of bias parameters to specify the relationship between the observed and unobserved variables and the sensitivity…