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

Blurring cluster randomized trials and observational studies using  Two-Stage TMLE to address sub-sampling, missingness, and minimal independent  units