When research questions require the use of precious samples, expensive assays or equipment, or labor-intensive data collection or analysis, nested case–control or case–cohort sampling of observational cohort study participants can often reduce costs. These study designs have similar statistical precision for addressing a singular research question, but case–cohort studies have broader efficiency and superior flexibility. Despite this, case–cohort designs are comparatively underutilized in the epidemiologic literature. Recent advances in statistical methods and software have made analyses of case–cohort data easier to implement, and advances from casual inference, such as inverse probability of sampling weights, have allowed the case–cohort design to be used with a variety of target parameters and populations. To provide an accessible link to this technical literature, we give a conceptual overview of case–cohort study analysis with inverse probability of sampling weights. We show how this general analytic approach can be leveraged to more efficiently study subgroups of interest or disease subtypes or to examine associations independent of case status. A brief discussion of how this framework could be extended to incorporate other related methodologic applications further demonstrates the broad cost-effectiveness and adaptability of case–cohort methods for a variety of modern epidemiologic applications in resource-limited settings.