Several authors have acknowledged that testing mediational hypotheses between treatments, genes, physiological measures, and behaviors may substantially advance our understanding of how these associations operate. In psychiatric research, the costs of measuring the putative mediator or the outcome can be prohibitive. Extreme sampling designs have been validated as methods for reducing study costs by increasing power per subject measured on the more expensive variable when assessing bivariate relationships. However, there exist concerns about how missing data can potentially bias the results. Additionally, most mediation analysis techniques presuppose the joint measurement of mediators and outcomes for all subjects. There have been limited methodological developments for techniques that can evaluate putative mediators in studies that have employed extreme sampling, resulting in missing data. We demonstrate that extreme (selective) sampling strategies can be beneficial in the context of mediation analyses. Handling the missing data with maximum likelihood (ML) resulted in minimal power loss and unbiased parameter estimates. We must be cautious, though, in recommending the ML approach for extreme sampling designs because it yielded inflated Type 1 error rates under some null conditions. Yet, the use of extreme sampling designs and methods to handle the resultant missing data presents a viable research strategy.