A comprehensive approach to target exact molecular weights and chemical compositions for multimonomeric statistical copolymers using a new controlled statistics method with reversible addition-fragmentation chain transfer free-radical (RAFT) polymerization is presented. The system chosen to illustrate this procedure is an acrylic quarterpolymer consisting of methyl acrylate, 2-carboxyethyl acrylate, 2-hydroxypropyl acrylate, and 2-propylacetyl acrylate, modeling a well-known macromolecule utilized to deliver poorly water-soluble drugs (hydroxypropyl methylcellulose acetate succinate, HPMCAS). The relative reactivities at 70 C between monomer pairs were measured and employed to predict the feed ratio necessary for synthesizing well-defined compositions based on the Walling-Briggs model. Application of Skeist's equations addressed compositional drift and anticipated the general monomer incorporation distribution as a function of conversion, which was verified experimentally. This new and simple paradigm combining both predictive models provides complementary synthetic and predictive tools for designing macromolecular chemical architectures with hierarchical control over spatially dependent structure-property relationships for complex applications such as oral drug delivery.