{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:59:21Z","timestamp":1790330361055,"version":"4.1.0"},"reference-count":71,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174207"],"award-info":[{"award-number":["72174207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Evidence Based Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To assess network meta\u2010analysis (NMA) model biases under different proportional hazards (PHs) scenarios, compare fitting and extrapolation performance for hazard ratios (HRs) using cancer survival data, identify metrics guiding model selection, and compare extrapolation strategies using parametric versus constant\u2010tail HRs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted 30 NMAs (five three\u2010arm trials, with each arm anchoring pairwise comparisons for overall survival and progression\u2010free survival), subgroup analyses assessed the PH assumption and curve types. Models included Cox\u2010PH, Fractional Polynomial, Royston\u2013Parmar (RP), Piecewise Exponential (PWE), and Parametric Survival Model (PSM). For extrapolation, we categorized models into those using parametric\u2010extrapolation HRs and those assuming constant\u2010tail HRs. Main indicator was sum of squared errors (SSE), alongside Bias, assessed for observed data (SSE\u2010O, Bias\u2010O), fitting (SSE\u2010F, Bias\u2010F), and fitting\u2010extrapolation (SSE\u2010FE, Bias\u2010FE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Model choice introduced much more uncertainty when the PH assumption was violated than when held. RP\u20102knot had the best HR fitting, comparable to RP\u20101knot; errors were 0.5\u2010 to 33.7\u2010fold higher for other models. RP\u20102knot and RP\u20101knot demonstrated the best fitting\u2010extrapolation performance. Friedman tests showed RP\u20102knot significantly outperformed other models in fitting, except RP\u20101knot, and in fitting\u2010extrapolation, except RP\u20101knot and Cox\u2010PH. SSE\u2010O and Bias\u2010O were highly correlated with model fitting (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      = 0.682\u20130.713) and moderately with fitting\u2010extrapolation. Within 2\u20133 years extrapolation window, constant\u2010tail HR models outperformed parametric\u2010extrapolation HR.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Model selection required more caution when the PH assumption failed. RP model showed promising fitting and extrapolation performance. SSE\u2010O and Bias\u2010O may guide model selection in HR\u2010fitting; extrapolation utility needs further validation. Non\u2010PH models using constant\u2010tail HRs showed better short\u2010term extrapolation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jebm.70183","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T02:29:57Z","timestamp":1787624997000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison and Selection of Network Meta\u2010Analysis Models for Fitting and Extrapolating Cancer Survival Data"],"prefix":"10.1111","volume":"19","author":[{"given":"Mingye","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Pharmacoeconomics School of International Pharmaceutical Business China Pharmaceutical University Nanjing China"},{"name":"Center for Pharmacoeconomics and Outcomes Research China Pharmaceutical University Nanjing 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