Table 1.

Examples of Differences in How Data from Conventional Randomized Controlled Trials and Real-World Evidence are Utilized.

Characteristic	RCTs	RWE
Standard of evidence	Gold standard	Complementary to RCTs
Cost	Costly to develop and conduct	Less costly
Patient population	Well-defined within constraints of specific inclusion criteria Results reflect outcomes in limited population	Broader and promotes evaluation of patient populations less often studied in clinical trials Patient data derived from other sources, including insurance claims
Sample size	Limited Requires sample size calculation to be performed in advance	Orders of magnitude larger
Efficacy	Randomized and blinding lead to minimized risk of data bias and confounding	Randomization and blinding may not be feasible Risk of unrecognized data bias and confounding greater
Adverse events	Only more frequently occurring adverse events revealed	Can reveal adverse events with much lower frequency and those requiring longer exposure to occur
Approval or clearance of new medical products	Considered the gold standard necessary for new drug approval, and when feasible for new device approval	Not generally accepted for approving new drugs but can complement RCT findings, accepted for new device indications
Role in diabetes	Define efficacy and provide a preliminary safety profile in a well-defined and controlled population	Allows estimation of more realistic treatment effects of a wide range of diabetes interventions and evaluation of interactions such as social determinants of health and comorbidities
Other issues	May be less useful when strong signals are available from RWE or early-phase trials	Facilitates postmarketing surveillance of adverse events and assessment of the product effectiveness Results may be less credible due when a control group is not included

Source: Adapted from Gyawali et al.⁷