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Decision Making Under Uncertainty: Comparing Regulatory and Health Technology Assessment Reviews of Medicines in the United States and Europe.

ABSTRACT: Assessments of clinical evidence vary between regulators and health technology assessment bodies, but precise differences remain unclear. To compare uncertainties raised on the clinical evidence of approved drugs, we analyzed assessments of regulators and health technology assessment (HTA) bodies in the United States and Europe. We found that US and European regulators report uncertainties related to safety for almost all drugs (85-94%), whereas HTA bodies reported these less (53-59%). By contrast, HTA bodies raised uncertainties related to effects against relevant comparators for almost all drugs (88-100%), whereas this was infrequently addressed by regulators (12-32%). Regulators as well as HTA bodies reported uncertainties related to the patient population for 60-95% of drugs. The patterns of regulator-HTA misalignment were comparable between the United States and Europe. Our results indicate that increased coordination between these complementary organizations is necessary to facilitate the collection of necessary evidence in an efficient and timely manner.

SUBMITTER: Vreman RA

PROVIDER: S-EPMC7484915 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Decision Making Under Uncertainty: Comparing Regulatory and Health Technology Assessment Reviews of Medicines in the United States and Europe.

Vreman Rick A RA Naci Huseyin H Goettsch Wim G WG Mantel-Teeuwisse Aukje K AK Schneeweiss Sebastian G SG Leufkens Hubert G M HGM Kesselheim Aaron S AS

Clinical pharmacology and therapeutics 20200420 2

Assessments of clinical evidence vary between regulators and health technology assessment bodies, but precise differences remain unclear. To compare uncertainties raised on the clinical evidence of approved drugs, we analyzed assessments of regulators and health technology assessment (HTA) bodies in the United States and Europe. We found that US and European regulators report uncertainties related to safety for almost all drugs (85-94%), whereas HTA bodies reported these less (53-59%). By contra ...[more]

PMID: 32236959

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Project description:BackgroundStatistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes.MethodsWe developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distribution is not needed; instead, the tipping point output is compared to reasonable effect ranges to draw conclusions. We demonstrate our approach in a comparative effectiveness analysis comparing two treatments in a real world biomarker-defined cohort, and provide guidelines for how to apply BAE in practice.ResultsOur initial comparative effectiveness analysis results in a hazard ratio of 0.31 with 95% confidence interval (0.09, 1.1). Applying BAE to this result yields a tipping point of 0.54; thus, an observed hazard ratio of 0.54 or smaller in a replication study would result in posterior credibility for the treatment association. Given that effect sizes in this range are not extreme, and that supportive evidence exists from a similar published study, we conclude that this problem is worthy of further research.ConclusionsOur proposed method provides a useful framework for interpreting analytic results from small datasets. This can assist researchers in deciding how to interpret and continue their investigations based on an initial analysis that has high uncertainty. Although we illustrated its use in estimating parameters based on time-to-event outcomes, BAE easily applies to any normally-distributed estimator, such as those used for analyzing binary or continuous outcomes.

Project description:Introduction: Although pharmaceutical companies, regulatory authorities, and health technology assessment (HTA) agencies have been increasingly using decision-making frameworks, it is not certain whether these enable better quality decision making. This could be addressed by formally evaluating the quality of decision-making process within those organizations. The aim of this literature review was to identify current techniques (tools, questionnaires, surveys, and studies) for measuring the quality of the decision-making process across the three stakeholders. Methods: Using MEDLINE, Web of Knowledge, and other Internet-based search engines, a literature review was performed to systematically identify techniques for assessing quality of decision making in medicines development, regulatory review, and HTA. A structured search was applied using key words and a secondary review was carried out. In addition, the measurement properties of each technique were assessed and compared. Ten Quality Decision-Making Practices (QDMPs) developed previously were then used as a framework for the evaluation of techniques identified in the review. Due to the variation in studies identified, meta-analysis was inappropriate. Results: This review identified 13 techniques, where 7 were developed specifically to assess decision making in medicines' development, regulatory review, or HTA; 2 examined corporate decision making, and 4 general decision making. Regarding how closely each technique conformed to the 10 QDMPs, the 13 techniques assessed a median of 6 QDMPs, with a mode of 3 QDMPs. Only 2 techniques evaluated all 10 QDMPs, namely the Organizational IQ and the Quality of Decision Making Orientation Scheme (QoDoS), of which only one technique, QoDoS could be applied to assess decision making of both individuals and organizations, and it possessed generalizability to capture issues relevant to companies as well as regulatory authorities. Conclusion: This review confirmed a general paucity of research in this area, particularly regarding the development and systematic application of techniques for evaluating quality decision making, with no consensus around a gold standard. This review has identified QoDoS as the most promising available technique for assessing decision making in the lifecycle of medicines and the next steps would be to further test its validity, sensitivity, and reliability.

Dataset Information

Decision Making Under Uncertainty: Comparing Regulatory and Health Technology Assessment Reviews of Medicines in the United States and Europe.

Publications

Decision Making Under Uncertainty: Comparing Regulatory and Health Technology Assessment Reviews of Medicines in the United States and Europe.

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