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医生处方偏好工具变量方法的评估：以二型糖尿病治疗为例AI 解读

Instrumental Variables provide a way of addressing bias due to unmeasured confounding when estimating treatment effects using observational data. As instrument prescription preference of individual healthcare providers has been proposed. Because prescription preference is hard to measure and often unobserved, a surrogate measure constructed from available data is often required for the analysis. Different construction methods for this surrogate measure are possible, such as…

观察性比较效果研究中的工具变量、混杂调整与双重差分三角验证AI 解读

Observational studies can play a useful role in assessing the comparative effectiveness of competing treatments. In a clinical trial the randomization of participants to treatment and control groups generally results in well-balanced groups with respect to possible confounders, which makes the analysis straightforward. However, when analysing observational data, the potential for unmeasured confounding makes comparing treatment effects much more challenging. Causal inference…