Pattern-Based Sequential Multiple Imputation for Missing Data in Clinical Trials: An Extension for Baseline-Only Early Dropout Subjects
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AI 根据所提供的论文材料归类：摘要明确以完整随机人群的治疗政策效应为目标，并比较缺失数据处理方案。
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临床试验序贯多重插补：仅有基线数据的早期退出者扩展
- 研究问题
- 临床试验中受试者在首次随访前退出、仅留下基线数据时，如何纳入完整随机人群并估计治疗政策目标量？
- 方法与思路
- 作者先使用同治疗组的协变量匹配供体补建数据，再扩展停药模式序贯插补；比较补全整条轨迹与仅补首访两种方案。
- 材料陈述的贡献
- 摘要称，模拟中仅补首访方案在信息性退出下更稳健；整轨迹重建则出现方差低估、覆盖下降及第一类错误膨胀。
- 阅读关注点
- 应核对供体匹配与随机性处理、退出机制设定、多重插补的方差合并方式，以及首访方案轻度错误率膨胀的范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；推荐主要基于所述模拟，摘要未明确缺失数据识别假设及适用边界。
原文摘要
Under the ICH E9 (R1) addendum, treatment policy strategies for intercurrent events target the treatment effect regardless of treatment discontinuation. Sequential multiple imputation (MI) models that condition each visit's imputation on discontinuation status or pattern reduce bias relative to mixed models and standard MI, but require every subject to contribute at least one post-baseline observation, an assumption violated by subjects who withdraw before any post-baseline assessment, a baseline-only early dropout pattern common in chronic-disease trials. We propose Extended Pattern-based Sequential Multiple Imputation (EPSMI), which reconstructs missing data for baseline-only early dropouts using covariate-matched, same-arm donors before applying an extended discontinuation-pattern indicator within eight sequential MI models. Two strategies were evaluated: EPSMI-Full, imputing the entire post-baseline trajectory from a donor, and EPSMI-Y1, imputing only the first visit and leaving later visits to the pattern-extended MI engine. A simulation study grounded in published Sjogren's syndrome trials evaluated bias, coverage, precision, power, and Type I error across 24 scenarios, comparing EPSMI against MMRM, standard MI, and sequential MI after excluding early dropouts (No Early). Under random early dropout, both EPSMI strategies reduced bias relative to No Early. Under informative early dropout the strategies diverged: EPSMI-Y1 remained robust, matching or exceeding No Early coverage with only mild Type I error inflation, whereas EPSMI-Full's deterministic reconstruction produced larger bias, narrower intervals from underestimated variance, lower coverage, and clear Type I error inflation. EPSMI-Y1 is recommended as the primary analysis strategy for baseline-only early dropout, keeping estimation faithful to the treatment policy estimand over the full randomized population.