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纳入部分伴发事件后观测的临床试验因果参考模型贝叶斯分析AI 解读

When treatment policy estimands are of interest, clinical trials often attempt to collect patient data after intercurrent events (ICEs), although such data are often limited. Retrieved dropout imputation methods, which use pre-ICE and available post-ICE data to impute missing post-ICE outcomes, are commonly applied but often yield treatment effect estimates with large standard errors (SEs) and may encounter convergence issues when post-ICE data are sparse. Reference-based…

临床试验缺失数据的因果参照模型贝叶斯分析AI 解读

The statistical analysis of clinical trials is often complicated by missing data. Patients sometimes experience intercurrent events (ICEs), which usually (although not always) lead to missing subsequent outcome measurements for such individuals. The reference-based imputation methods were proposed by Carpenter et al. (2013) and have been commonly adopted for handling missing data due to ICEs when estimating treatment policy strategy estimands. Conventionally, the variance…