Randomized Controlled Trials Versus Real World Evidence: Neither Magic Nor Myth

Hans-Georg Eichler, Francesco Pignatti, Brigitte Schwarzer-Daum, Ana Hidalgo-Simon, Irmgard Eichler, Peter Arlett, Anthony Humphreys, Spiros Vamvakas, Nikolai Brun, Guido Rasi, Hans-Georg Eichler, Francesco Pignatti, Brigitte Schwarzer-Daum, Ana Hidalgo-Simon, Irmgard Eichler, Peter Arlett, Anthony Humphreys, Spiros Vamvakas, Nikolai Brun, Guido Rasi

Abstract

Compared with drugs from the blockbuster era, recently authorized drugs and those expected in the future present a heterogenous mix of chemicals, biologicals, and cell and gene therapies, a sizable fraction being for rare diseases, and even individualized treatments or individualized combinations. The shift in the nature of products entails secular trends for the definitions of "drugs" and "target population" and for clinical use and evidence generation. We discuss that the lessons learned from evidence generation for 20th century medicines may have limited relevance for 21st century medicines. We explain why the future is not about randomized controlled trials (RCTs) vs. real-world evidence (RWE) but RCTs and RWE-not just for the assessment of safety but also of effectiveness. Finally, we highlight that, in the era of precision medicine, we may not be able to reliably describe some small treatment effects-either by way of RCTs or RWE.

Conflict of interest statement

The authors declared no competing interests for this work.

© 2020 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

Figures

Figure 1
Figure 1
The complex matrix of research questions and methods. The graphic conceptualizes the complexities of research questions associated with a hypothetical drug treatment intended for a disease condition caused by different mutations in individual patients. Complexity is defined along three axes: the x‐axis depicts breadth of information (i.e., different patient subgroups, based on mutation, phenotype, or disease stage), the z‐axis depicts depth of information (i.e., different types of efficacy or safety end points of interest), and the y‐axis depicts context of information (i.e., different comparators or treatment combinations). Each cell in the three‐dimensional matrix represents an item of information that may be relevant for a particular decision maker and/or patient subgroup. Different study types (symbolized by different colors) will be required to generate the information, given the appropriateness of methods for different research questions as well as practical constraints on evidence generation. Note that for some research questions, there will be no data and information available at all, at least at the time of market launch. See main text for real‐life examples that fit the schematic. ECA, external control arm; RCT, randomized controlled trial.

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