AI 中文总结
本研究针对临床试验中仅基线期早期退出的缺失数据，提出扩展的基于模式的多重顺序插补（EPSMI）方法，经模拟验证EPSMI-Y1策略表现稳健，推荐作为此类情况的主要分析策略。
AI 中文摘要
根据ICH E9 (R1)附录，针对并发事件的治疗策略旨在无论治疗中断与否均评估治疗效果。多重顺序插补（MI）模型通过将每次访视的插补基于中断状态或模式来减少混合模型和标准MI带来的偏差，但该模型要求每位受试者至少提供一次基线后观察值，而在慢性病试验中常见的仅基线期早期退出模式（即受试者在任何基线后评估前即退出）会违反这一假设。我们提出了扩展的基于模式的多重顺序插补（EPSMI），该方法使用协变量匹配的同组供体来重构仅基线期早期退出受试者的缺失数据，之后在8个顺序MI模型中应用扩展的中断模式指标。我们评估了两种策略：EPSMI-Full，即从供体处插补整个基线后轨迹；EPSMI-Y1，即仅插补首次访视，将后续访视留给模式扩展的MI引擎。一项基于已发表干燥综合征试验的模拟研究，在24种场景下评估了偏差、覆盖率、精度、功效和I类错误，将EPSMI与MMRM、标准MI以及排除早期退出后的顺序MI（No Early）进行了比较。在随机早期退出情况下，两种EPSMI策略均比No Early减少了偏差；在信息性早期退出情况下，策略出现分化：EPSMI-Y1保持稳健性，匹配或超过No Early的覆盖率，仅出现轻度I类错误膨胀，而EPSMI-Full的确定性重构产生更大偏差、低估方差导致区间更窄、覆盖率更低以及明显的I类错误膨胀。建议将EPSMI-Y1作为仅基线期早期退出的主要分析策略，使估计值在整个随机化人群中忠实于治疗政策目标值。
英文摘要
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.
