Cluster randomized trials (CRTs) often enroll large numbers of participants, but due to logistical and fiscal challenges, only a subset of participants may be selected for measurement of certain outcomes, and those sampled may, purposely or not, be unrepresentative of all participants. Missing data also present a challenge: if sampled individuals with measured outcomes are dissimilar from those with missing outcomes, unadjusted estimates of arm-specific outcomes and the intervention effect may be biased. Further, CRTs often enroll and randomize few clusters by necessity, limiting statistical power and raising concerns about finite sample performance. Motivated by a sub-study of the SEARCH community randomized trial on the incidence of TB infection, we demonstrate interlocking methods to handle these challenges. First, we extend Two-Stage targeted minimum loss-based estimation (TMLE) to account for three sources of missingness: (1) sampling for the sub-study; (2) measurement of baseline status among those sampled, and (3) measurement of final status among those in the incidence cohort (i.e., persons known to be at risk at baseline). Second, we critically evaluate the assumptions under which sub-units of the cluster can be considered the conditionally independent unit, improving precision and statistical power but also causing the CRT to behave more like an observational study. Our application to the SEARCH highlights the impact of different assumptions on measurement and dependence as well as the real-life gains of our approach for bias reduction and efficiency improvement.

翻译：集群随机试验（CRT）常招募大量参与者，但受限于后勤与资金挑战，可能仅选择部分参与者测量特定结局，且这些被抽样者可能有意或无意地无法代表全体参与者。缺失数据同样构成挑战：若被抽样且已测量结局的个体与缺失结局的个体存在系统性差异，则未经调整的组别特异性结局估计值和干预效应可能产生偏差。此外，CRT常因实际需要仅对少量集群进行随机化，这限制了统计功效并引发对有限样本性能的担忧。受SEARCH社区随机试验中关于结核感染发生率的子研究启发，我们展示了应对这些挑战的联动方法体系。首先，我们扩展两阶段目标最小损失估计（TMLE）以处理三类缺失来源：(1)子研究的抽样缺失；(2)被抽样者基线状态的测量缺失；(3)发病率队列成员（即基线时已知处于风险的人群）最终状态的测量缺失。其次，我们严格评估了将集群内子单元视为条件独立单元所需满足的假设——这虽能提升精度与统计功效，却也使CRT更趋近于观察性研究。基于SEARCH数据的应用表明，不同测量假设与依赖性假设的影响显著，且我们的方法在偏差降低与效率提升方面具有实际增益。