Real world data are needed to drive decision-making by complementing randomised controlled trial (RCT) data generated from homogenous populations which may not reflect real-world drug effectiveness.1 Disease frequencies can be enumerated through a variety of methodologies including literature reviews and surveillance data. When primary incidence and prevalence data generation is necessary, a number of different study designs can be leveraged. Cross-sectional studies (CSS) serve as an effective study design option to gather pertinent epidemiological data quickly to support drug development across the lifecycle. This type of study, which includes surveys and prevalence studies, is the mainstay study design for surveying large populations to quantify the incidence and prevalence of health conditions, and/or population attributes such as risk factors.
However, the CSS design can be used to complement other study designs including medical chart review (MCR) studies. Here we explore how a hybrid MCR combined CSS can be an advantageous study design for real world evidence (RWE) generation, and how gaining a better understanding of the implementation of these designs can allow for better future planning, as well as to guide the development of the next generation of real world studies.