Characteristics of Public Comments Submitted to State Health Technology Assessment Programs in Oregon and Washington

Ahn, Rosa; Herrera-Perez, Diana; Prasad, Vinay

doi:10.1001/jamainternmed.2019.5145

Cited by 5 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also examined the citation of scientific evidence, adding to literature documenting lack of scientific evidence in public comments in other contexts. 10 The raw data from this study are available publicly, so that readers can view specific financial ties, evidence cited or reanalyse the data if desired. Finally, the AI/ML regulatory framework is still under development, and these findings may motivate better disclosure and use of scientific evidence as it continues to be developed.…”

Section: Discussionmentioning

confidence: 99%

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

et al. 2020

View full text Add to dashboard Cite

ObjectivesTo determine the extent and disclosure of financial ties to industry and use of scientific evidence in comments on a US Food and Drug Administration (FDA) regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD).DesignCross-sectional study.SettingWe searched all publicly available comments on the FDA ‘Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)—Discussion Paper and Request for Feedback’ from 2 April 2019 to 8 August 2019.Main outcome measuresThe proportion of articles submitted by parties with financial ties to industry, disclosing those ties, citing scientific articles, citing systematic reviews and meta-analyses, and using a systematic process to identify relevant literature.ResultsWe analysed 125 comments submitted on the proposed framework. 79 (63%) comments came from parties with financial ties; for 36 (29%) comments, it was not clear and the absence of financial ties could only be confirmed for 10 (8%) comments. No financial ties were disclosed in any of the comments that were not from industry submitters. The vast majority of submitted comments (86%) did not cite any scientific literature, just 4% cited a systematic review or meta-analysis and no comments indicated that a systematic process was used to identify relevant literature.ConclusionsFinancial ties to industry were common and undisclosed, and scientific evidence, including systematic reviews and meta-analyses, were rarely cited. To ensure regulatory frameworks best serve patient interests, the FDA should mandate disclosure of potential conflicts of interest (including financial ties) in comments, encourage the use of scientific evidence, and encourage engagement from non-conflicted parties.

show abstract

Section: Discussionmentioning

confidence: 99%

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We used a thorough method for identification of financial ties which has identified more potentially conflicted parties than would be apparent from the self-identification of the commenters. We also examined the citation of scientific evidence, adding to literature documenting lack of scientific evidence in public comments in other contexts 10 . The raw data from this study is available publicly, so that readers can view specific financial ties, evidence cited, or reanalyse the data if desired.…”

Section: Discussionmentioning

confidence: 99%

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

Smith

Abhari

Hussain

et al. 2019

Preprint

View full text Add to dashboard Cite

Objectives To determine the extent and disclosure of financial ties to industry and use of scientific evidence in comments on a US Food and Drug Administration (FDA) regulatory framework for modifications to artificial intelligence/machine Learning (AI/ML)-based software as a medical device (SaMD). Design Cross-sectional study. Setting We searched all publicly available comments on the FDA "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback" from April 2nd 2019 to August 8th 2019. Main outcome measures The proportion of articles submitted by parties with financial ties to industry, disclosing those ties, citing scientific articles, citing systematic reviews and meta-analyses, and using a systematic process to identify relevant literature. Results We analysed 125 comments submitted on the proposed framework. 79 (63%) comments came from parties with financial ties; for 36 (29%) comments it was not clear and the absence of financial ties could only be confirmed for 10 (8%) comments. No financial ties were disclosed in any of the comments that were not from industry submitters. The vast majority of submitted comments (86%) did not cite any scientific literature, just 4% cited a systematic review or meta-analysis, and no comments indicated that a systematic process was used to identify relevant literature. Conclusions Financial ties to industry were common and undisclosed and scientific evidence, including systematic reviews and meta-analyses, were rarely cited. To ensure regulatory frameworks best serve patient interests, the FDA should mandate disclosure of potential conflicts of interest (including financial ties), in comments, encourage the use of scientific evidence and encourage engagement from non-conflicted parties.

show abstract

“…Furthermore, a study from the United States demonstrated that financial conflicts of interest might also extend to patient representatives and advocacy groups [ 5 ]. These challenges, among others, translate into a growing skepticism for the reported benefit of new cancer drugs [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Describing Sources of Uncertainty in Cancer Drug Formulary Priority Setting across Canada

et al. 2021

View full text Add to dashboard Cite

Over the years, there have been significant advances in oncology. However, the rate that therapeutics come to market has increased, while the strength of evidence has decreased. Currently, there is limited understanding about how these uncertainties are managed in provincial funding decisions for cancer therapeutics. We conducted qualitative interviews with six senior officials from four different Canadian provinces (British Columbia, Alberta, Quebec, and Ontario) and a document review of the uncertainties found in submissions to the pan-Canadian Oncology Drug Review (pCODR). Participants reported considerable uncertainty related to a lack of solid clinical evidence (early-phase clinical trials: generalizability, immature data, and the use of unvalidated surrogate outcomes). Proposed strategies to deal with the uncertainty included risk-sharing agreements, collection of real-world evidence (RWE), and ongoing collaboration between federal groups and provinces. The document review added to the reported uncertainties by classifying them into five main categories: trial validity, population, comparators, outcomes, and intervention. This study highlights how decision makers must deal with significant amounts of uncertainty in funding decisions for cancer drugs, most of which stems from methodological limitations in clinical trials. There is a critical need for transparent priority-setting processes and mechanisms to reevaluate drugs to ensure benefit given the high level of uncertainty of novel therapeutics.

show abstract

Characteristics of Public Comments Submitted to State Health Technology Assessment Programs in Oregon and Washington

Abstract: reports receiving compensation fromMcKinsey & Company for providing strategy consulting to hospitals, insurance companies, and pharmaceutical companies on issues unrelated to this article.

Cited by 5 publications

References 0 publications

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study

Describing Sources of Uncertainty in Cancer Drug Formulary Priority Setting across Canada

Contact Info

Product

Resources

About