{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T09:23:26Z","timestamp":1793179406421,"version":"4.5.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The rising cost of cancer drugs places a heavy burden on patients and public healthcare systems, posing an ongoing challenge of balancing access with efficient resource allocation. Health technology assessment (HTA) serves as a critical basis for reimbursement decisions, evaluating both clinical and economic value. Although the Federal Joint Committee (G-BA) and the National Institute for Health and Care Excellence (NICE) have established standardized HTA frameworks, differences in their assessment methods and decision-making processes may lead to divergent outcomes. This study compares key factors influencing cancer drug assessments by G-BA and NICE and identifies drivers of discrepancies between the two agencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We reviewed 171 matched drug-indication pairs of cancer drugs assessed by G-BA and NICE between 1 January 2020 and 1 June 2025. Logistic regression identified key factors influencing outcomes, while decision tree models explored pathways through which these factors shaped results. The first regression further examined determinants of discordant HTA outcomes between the two agencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among analyzed drugs, NICE issued positive recommendations in 90.64% of cases, whereas G-BA granted an additional benefit in only 59.06%. G-BA outcomes were significantly associated with improvements in mortality (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.018), side effects (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.043), and orphan drug designation (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.0001). NICE recommendations were primarily influenced by low risk of bias (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.028) and incremental cost-effectiveness ratios (ICERs) \u2264 \u00a330,000 per QALY (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.054). Concordant HTA outcomes between G-BA and NICE were more likely when evidence was derived from randomized controlled trials (RCTs;\n                      <jats:italic>P<\/jats:italic>\n                      = 0.0003), demonstrated a low risk of bias (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.003), or showed improvements in mortality (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.003) and morbidity (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.009).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Despite differences in evaluation frameworks and institutional settings across countries, high-quality clinical evidence remains the central foundation for concordant HTA decisions. The findings provide policy-relevant insights for optimizing reimbursement policies and improving the efficiency of healthcare resource allocation. Moreover, they offer important implications for policymakers and manufacturers by clarifying the key drivers of HTA decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1733239","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:10:04Z","timestamp":1773756604000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A comparative analysis of health technology assessment decision-making for cancer drugs in Germany and England"],"prefix":"10.3389","volume":"14","author":[{"given":"Jiaqi","family":"He","sequence":"first","affiliation":[{"name":"School of Business Administration, Shenyang Pharmaceutical University","place":["Shenyang, China"]},{"name":"School of Pharmaceutical Sciences, Tsinghua University","place":["Beijing, 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Pharmaceutical Sciences, Soochow University","place":["Suzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Tsinghua University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Business Administration, Shenyang Pharmaceutical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1016\/S1470-2045(24)00516-3","article-title":"Rising costs of cancer medicines","volume":"25","author":"Devi","year":"2024","journal-title":"Lancet 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