LAUSR

In pursuit of efficiency and speed in drug development, trialists are progressively pivoting on the use of real-world data (RWD) or real-world evidence (RWE), especially in the areas of high unmet medical need (Food and Drug Administration (FDA) 2017a, 2017b, 2019, 2020); on the other hand, regulatory agencies continue to encourage the adoption of RWD/RWE by establishing rigorous criteria for its use in decision-making and have devised new guidelines to facilitate uptake. Pursuant to the 21 Century Cures Act section 3022 (e) (21st Century Cures Act 2016) or section 505 F(e) of the Federal Food and Drug Cosmetic (FD&C) Act, by mid-December 2021, the US Food and Drug Administration (FDA) released several draft guidance pertinent to the use of RWD and RWE to support regulatory decision-making (Food and Drug Administration 2021a, 2021b, 2021c, 2021d). Of note, these guidance only remark on the use of RWE in underpinning the approval for new indication for already approved drugs under section 505(c) of the FD&C Act and to help support or satisfy post-approval study requirements. Meanwhile, in September 2021, FDA held a public meeting to discuss PDUFA VII reauthorization, where improving quality and acceptability of RWE is one key topic of pre-market review to expediate drug development. Imminently, the 21 Century Cures 2.0, currently available as discussion draft (Cures 2.0 Discussion Draft 2021), promotes the maximization and expansion of the use of RWE, including fulfilling post-approval requirements for accelerated approval programs. In light of the increasingly encouraging environment, the Journal of Biopharmaceutical Statistics publicly called for submission of methodological or review papers to a special issue on RWE, in an effort to promote statistical methodology innovation in this area. Remarkably, the final compilation shows an assemblage of ingenious, informative, and acutely insightful articles by authors from industry, government, and academia. Out of the thirteen selected peer-reviewed papers, notably, eight employed Bayesian approaches and eight utilized propensity scores. Furthermore, a panoply of topics were articulated in the methodological papers: entropy balancing (Yu et al. 2022), small-sized trial (Jiao et al. 2022), resampling approaches for estimating similarity measure (Li et al. 2022), sampling importance resampling method for borrowing (Sachdeva et al. 2021), propensity scorebased prior construction (Baron et al. 2022; Lu et al. 2021), deep learning (Zhan et al. 2021), combining the survival data reconstruction technique with balancing weights (Getz et al. 2021), and intermediate outcome-assisted borrowing (Liu et al. 2022). Additionally, a comprehensive and insightful review of propensity score methods under the Bayesian framework was provided by Lin and Lin (2021), and extensive simulation studies on different propensity score adjustment methods with various prior constructions were conducted by Wang et al. (2021); a case example of propensity score matching was illustrated by Yin et al. (2022). Our sincere gratitude goes to the authors for their creative thinking and contributions germane to generating and using RWE and to the referees for the devotion of their time and wisdom to the service to the scientific community. Last but not the least, our special appreciation goes to