The present and future scope of real‐world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

Abstract

The last decade has witnessed an exponential growth in the opportunities to collect and link health‐related data from multiple resources, including primary care, administrative, and device data. The availability of these “real‐world,” “big data” has fuelled also an intense methodological research into methods to handle them and extract actionable information. In medicine, the evidence generated from “real‐world data” (RWD), which are not purposely collected to answer biomedical questions, is commonly termed “real‐world evidence” (RWE). In this review, we focus on RWD and RWE in the area of diabetes research, highlighting their contributions in the last decade; and give some suggestions for future RWE diabetes research, by applying well‐established and less‐known tools to direct RWE diabetes research towards better personalized approaches to diabetes care. We underline the essential aspects to consider when using RWD and the key features limiting the translational potential of RWD in generating high‐quality and applicable RWE. Only if viewed in the context of other study designs and statistical methods, with its pros and cons carefully considered, RWE will exploit its full potential as a complementary or even, in some cases, substitutive source of evidence compared to the expensive evidence obtained from randomized controlled trials.

Insulin glargine 300 units/mL for the treatment of individuals with type 2 diabetes in the real world: A review of the DELIVER programme

“…They can also highlight differences between guideline recommendations and implementation. 10,24 Furthermore, findings from observational RWE studies can be hypothesis-generating and can be used to design further prospective studies. 10 Consequently, RWE has increasingly become recognized as a valuable source of information on the safety and effectiveness of therapies.…”

“…31,32 Further attempts to improve the quality of RWE include designing such studies with the same methodological rigour as RCTs, including detailed study protocols and statistical analysis plans, and comprehensive reporting of data sources, data ranges, exclusion criteria, care setting exposure to study drugs, outcomes, attrition and analytical strategies. 24,33 In addition, the validity of RWE would be further supported if consistency can be shown across studies utilizing similar data sources (e.g. EHR), as well as those utilizing different data sources (e.g.…”

“…RWE can also provide information beyond treatment effectiveness, such as cost‐effectiveness, HCRU and geographic patterns of prescription data. They can also highlight differences between guideline recommendations and implementation 10,24 . Furthermore, findings from observational RWE studies can be hypothesis‐generating and can be used to design further prospective studies 10 …”

“…They can also highlight differences between guideline recommendations and implementation. 10 , 24 Furthermore, findings from observational RWE studies can be hypothesis‐generating and can be used to design further prospective studies. 10 …”

Spotlight commentary: A role for real‐world evidence to inform the clinical care of patients with diabetes mellitus

“…Salvo and Faillie 2 make the case that a real-world assessment of antihyperglycaemic treatments is needed given the predominance of surrogate endpoints to study beneficial drug effects, the exclusion of older and/or multimorbid persons from trials, and the inability of trials to examine uncommon safety outcomes. Zaccardi et al 3 envision that RWE in diabetes can foster a path toward personalized medicine using newer and more powerful data-driven tools. a requirement for cases to have static insulin regimens in the 6 months prior to their event; and a lack of conditioning on matching in the logistic regression analysis.…”