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      Strategies to Address Current Challenges in Real-World Evidence Generation in Japan

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          Abstract

          The generation of real-world evidence (RWE), which describes patient characteristics or treatment patterns using real-world data (RWD), is rapidly growing more popular as a tool for decision-making in Japan. The aim of this review was to summarize challenges to RWE generation in Japan related to pharmacoepidemiology, and to propose strategies to address some of these challenges. We first focused on data-related issues, including the lack of transparency of RWD sources, linkage across different care settings, definitions of clinical outcomes, and the overall assessment framework of RWD when used for research purposes. Next the study reviewed methodology-related challenges. As lack of design transparency impairs study reproducibility, transparent reporting of study design is critical for stakeholders. For this review, we considered different sources of biases and time-varying confounding, along with potential study design and methodological solutions. Additionally, the implementation of robust assessment of definition uncertainty, misclassification, and unmeasured confounders would enhance RWE credibility in light of RWD source-related limitations, and is being strongly considered by task forces in Japan. Overall, the development of guidance for best practices on data source selection, design transparency, and analytical methods to address different sources of biases and robustness in the process of RWE generation will enhance credibility for stakeholders and local decision-makers.

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          Sensitivity Analysis in Observational Research: Introducing the E-Value.

          Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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            Semiparametric Difference-in-Differences Estimators

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              Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective.

              Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced anew generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m > 1 plausible values. The rn versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997a) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from the Adolescent Alcohol Prevention Trial (Hansen & Graham, 199 I).
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                Author and article information

                Contributors
                takahiro_hirano@jp-css.com
                Journal
                Drugs Real World Outcomes
                Drugs Real World Outcomes
                Drugs - Real World Outcomes
                Springer International Publishing (Cham )
                2199-1154
                2198-9788
                13 May 2023
                13 May 2023
                : 1-10
                Affiliations
                [1 ]GRID grid.258269.2, ISNI 0000 0004 1762 2738, Real-World Evidence and Data Assessment (READS), Graduate School of Medicine, , Juntendo University, ; Hongo 2-1-1, Bunkyo-ku, Tokyo, 113-8421 Japan
                [2 ]Clinical Study Support Inc., 2F Daiei Bldg., 1-11-20 Nishiki Naka-ku, Nagoya, 460-0003 Japan
                [3 ]Real-World Evidence, Evidera, The Ark, 2nd Floor, 201 Talgarth Road, London, W6 8BJ UK
                [4 ]GRID grid.258269.2, ISNI 0000 0004 1762 2738, Department of Radiology, School of Medicine, , Juntendo University, ; Hongo 2-1-1, Bunkyo-ku, Tokyo, 113-8421 Japan
                Author information
                http://orcid.org/0000-0002-3803-0809
                Article
                371
                10.1007/s40801-023-00371-5
                10182751
                37178273
                1b5f029b-6658-4465-8265-c40383a8200f
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 19 April 2023
                Categories
                Review Article

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