Dataset Information

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model.

ABSTRACT: Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the integration of RWE into regulatory decision-making process from the perspective of DRAs. Different development strategies to develop and adopt RWE by the DRAs in the US, Europe, and China were reviewed and compared, and the challenges encountered were discussed. It was found that different strategies on development of RWE were applied by FDA, EMA, and NMPA. The extent to which RWE was adopted in China was relatively limited compared to that in the US and EU, which was highly related to the national pharmaceutical environment and development stages. A better understanding of the overall goals, inputs, activities, outputs, and outcomes in developing RWE will help inform actions to harness RWD and leverage RWE for better health care decisions.

SUBMITTER: Li M

PROVIDER: S-EPMC8200400 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model.

Li Meng M Chen Shengqi S Lai Yunfeng Y Liang Zuanji Z Wang Jiaqi J Shi Junnan J Lin Haojie H Yao Dongning D Hu Hao H Ung Carolina Oi Lam COL

Frontiers in medicine 20210531

Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the in ...[more]

PMID: 34136505

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Project description:IntroductionA review of European Union (EU)-funded initiatives linked to 'Real World Evidence' (RWE) was performed to determine whether their outputs could be used for the generation of real-world data able to support the European Medicines Agency (EMA)'s regulatory decision-making on medicines.MethodThe initiatives were identified from publicly available websites. Their topics were categorised into five areas: 'Data source', 'Methodology', 'Governance model', 'Analytical model' and 'Infrastructure'. To assess their immediate relevance for medicines evaluation, their therapeutic areas were compared with the products recommended for EU approval in 2016 and those included in the EMA pharmaceutical business pipeline.ResultsOf 171 originally identified EU-funded initiatives, 65 were selected based on their primary and secondary objectives (35 'Data source' initiatives, 15 'Methodology', 10 'Governance model', 17 'Analytical model' and 25 'Infrastructure'). These 65 initiatives received over 734 million Euros of public funding. At the time of evaluation, the published outputs of the 40 completed initiatives did not always match their original objectives. Overall, public information was limited, data access was not explicit and their sustainability was unclear. The topics matched 8 of 14 therapeutic areas of the products recommended for approval in 2016 and 8 of 15 therapeutic areas in the 2017-2019 pharmaceutical business pipeline. Haematology, gastroenterology or cardiovascular systems were poorly represented.ConclusionsThis landscape of EU-funded initiatives linked to RWE which started before 31 December 2016 highlighted that the immediate utilisation of their outputs to support regulatory decision-making is limited, often due to insufficient available information and to discrepancies between outputs and objectives. Furthermore, the restricted sustainability of the initiatives impacts on their downstream utility. Multiple projects focussing on the same therapeutic areas increase the likelihood of duplication of both efforts and resources. These issues contribute to gaps in generating RWE for medicines and diminish returns on the public funds invested.

Project description:Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantum entanglement.

Dataset Information

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model.

Publications

Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model.

Similar Datasets

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