{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:50:52Z","timestamp":1791820252112,"version":"4.3.4"},"reference-count":29,"publisher":"American Medical Association (AMA)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Intern Med"],"abstract":"\n Importance<\/jats:title>\n Cancer drugs are frequently approved based on uncertain benefits and risks that are not communicated to patients.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate the effect of communicating uncertainties about the benefits and risks of new cancer drugs on changes in individuals\u2019 hypothetical decisions to take the drug.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n Two randomized clinical trials recruited online a demographically diverse sample of US adults (census matched for age, sex, education, income, and race and ethnicity) from May 9 to July 26, 2025.<\/jats:p>\n <\/jats:sec>\n \n Intervention<\/jats:title>\n Plain-language statements describing uncertainties about drug benefits and risks. Trial 1 randomized participants to 1 of 5 uncertainties: (1) single-arm trial design, (2) unrepresentative population in terms of race and ethnicity, (3) short study duration, (4) surrogate end point, or (5) no noticeable improvement. Trial 2 randomized participants to 1, 2, 3, or 4 uncertainties or to a control group.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n The primary outcome in trial 1 was changes in hypothetical decisions to take a new cancer drug (within-group difference), while in trial 2 the primary outcome was the change in between-group differences in hypothetical decisions (control vs intervention groups). Secondary outcomes included perceived treatment effectiveness, worry associated with taking the drug, and trust in the information and the US Food and Drug Administration.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A total of 6272 adults were included in the primary analysis (3102 [49.5%] women). In trial 1, learning about uncertainties reduced hypothetical decisions to take the drug by a mean of 10.1 percentage points (pp; 95% CI, \u221211.7 to \u22128.5 pp;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). The reduction in hypothetical decisions to take the drug was observed for all uncertainties, including among those in the group that learned that it was unknown whether patients would notice an improvement with the drug (\u221214.1 pp; 95% CI, \u221217.9 to \u221210.4 pp;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001) and those who learned that the drug was not studied in patients of similar race or ethnicity (\u221212.8 pp; 95% CI, \u221216.4 to \u22129.2;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). In trial 2, hypothetical decisions to take the drug decreased with each additional uncertainty, demonstrating a dose-dependent effect. Learning about uncertainties reduced perceived effectiveness and trust but did not affect worry.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In 2 randomized clinical trials including a demographically diverse sample of US adults, learning about common uncertainties with a new cancer drug\u2019s benefits and risks reduced hypothetical decisions to take the drug.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifiers:\n NCT06997185<\/jats:ext-link>\n and\n NCT07110597<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamainternmed.2026.4969","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:30:37Z","timestamp":1791819037000},"source":"Crossref","is-referenced-by-count":0,"title":["Communicating Uncertainties About the Benefits and Risks of New Cancer 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Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florence T.","family":"Bourgeois","sequence":"additional","affiliation":[{"name":"Pharmaceutical Policy Lab, LSE Health, London School of Economics and Political Science, London, United Kingdom"},{"name":"Computational Health Informatics Program, Boston Children\u2019s Hospital, Boston, Massachusetts"},{"name":"Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elias","family":"Mossialos","sequence":"additional","affiliation":[{"name":"Department of Health Policy, London School of Economics and Political Science, London, United Kingdom"},{"name":"Pharmaceutical Policy Lab, LSE Health, London School of Economics and Political Science, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huseyin","family":"Naci","sequence":"additional","affiliation":[{"name":"Department of Health 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