Real-world data is needed to guide decision-making by supplementing randomized controlled trial (RCT) data generated from homogeneous populations that may not reflect actual drug efficacy.1 Disease frequencies may be listed using a variety of methodologies, including literature reviews and surveillance data. . When the generation of primary data on incidence and prevalence is required, a number of different study designs can be exploited. Cross-sectional studies (CSS) are an effective study design option for quickly collecting relevant epidemiological data to support drug development throughout the lifecycle. This type of study, which includes surveys and prevalence studies, is the baseline study design for investigating large populations in order to quantify the incidence and prevalence of health problems and / or disease attributes. population such as risk factors.
However, the CSS design can be used to complement other study designs, including medical record review (MCR) studies. Here, we explore how a combined MCR hybrid CSS can be an advantageous study design for Real World Evidence Generation (RWE), and how a better understanding of the implementation of these designs can enable better future planning, as well as guiding the development of the next generation of real world studies.