In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster‐randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation–maximization algorithm to estimate the SACE in cluster‐randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster‐level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed‐effects approach to illustrate the importance of accounting for clustering in cluster‐randomized trials. The methodology is then illustrated using a cluster‐randomized trial of telecare and assistive technology on health‐related QOL in the elderly.