Partially clustered designs are widely used in psychological research, especially in randomized controlled trials that examine the effectiveness of prevention or intervention strategies. In a partially clustered trial, individuals are clustered into intervention groups in one or more study arms, for the purpose of intervention delivery, whereas individuals in other arms (e.g., the waitlist control arm) are unclustered. Missing data are almost inevitable in partially clustered trials and could pose a major challenge in drawing valid research conclusions. This article focuses on handling auxiliary-variable-dependent missing at random data in partially clustered studies. Five methods were compared via a simulation study, including simultaneous multiple imputation using joint modeling (MI-JM-SIM), arm-specific multiple imputation using joint modeling (MI-JM-AS), arm-specific multiple imputation using substantive-model-compatible sequential modeling (MI-SMC-AS), sequential fully Bayesian estimation using noninformative priors (SFB-NON), and sequential fully Bayesian estimation using weakly informative priors (SFB-WEAK). The results suggest that the MI-JM-AS method outperformed other methods when the variables with missing values only involved fixed effects, whereas the MI-SMC-AS method was preferred if the incomplete variables featured random effects. Applications of different methods are also illustrated using an empirical data example. (PsycInfo Database Record (c) 2023 APA, all rights reserved).