{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T19:14:39Z","timestamp":1788290079549,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012325","name":"National Office for Philosophy and Social Sciences","doi-asserted-by":"publisher","award":["24BTJ059"],"award-info":[{"award-number":["24BTJ059"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>While randomized controlled trials remain the gold standard for estimating causal effects in medical research, they are not always practical. Consequently, observational data become a necessary alternative, though it introduces confounding bias due to the lack of randomization. Propensity score modeling mitigates this bias but is highly sensitive to covariate selection. To address this critical limitation in survival analysis, we propose the outcome\u2010adaptive Lasso (OAL)\u2013Cox model\u2014a novel variable selection framework for causal inference for right\u2010censored data. The proposed method integrates the OAL with the Cox proportional hazards model by constructing penalty weights from coefficients estimated through the Cox partial likelihood. This design enables OAL\u2010Cox to effectively identify outcome\u2010related confounders while excluding irrelevant covariates, including instrumental and spurious variables, even under censoring and correlated covariate structures. Simulation studies demonstrate that OAL\u2010Cox achieves stable variable selection across different censoring rates and correlation settings. Compared with the L\u2010Cox and AL\u2010Cox methods considered in this study, OAL\u2010Cox more effectively reduces the inclusion of instrumental and spurious covariates while retaining outcome\u2010related covariates, and it generally yields more stable restricted mean survival time\u2010based causal effect estimates in the examined settings. We further illustrate the practical applicability of the proposed method by applying it to ovarian cancer recurrence time data to evaluate the effect of surgical sophistication on recurrence\u00a0time.<\/jats:p>","DOI":"10.1002\/bimj.70168","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:25:35Z","timestamp":1788287135000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Outcome\u2010Adaptive Lasso for Cox Proportional Hazards Model"],"prefix":"10.1002","volume":"68","author":[{"given":"Fan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Biostatistics School of Public Health Fudan University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5556-1835","authenticated-orcid":false,"given":"Juan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Central South University Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6938-9507","authenticated-orcid":false,"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Central South University Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-BA1131"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00273171.2011.568786"},{"key":"e_1_2_11_4_1","first-page":"1","article-title":"Establishment of a Novel Glycolysis\u2010Related Prognostic Gene Signature for Ovarian Cancer and Its Relationships With Immune Infiltration of the Tumor Microenvironment","volume":"19","author":"Bi J.","year":"2021","journal-title":"Journal of Translational Medicine"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwj149"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8206"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/09622802241306856"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1977.10480613"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2013.823775"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280215604510"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1515\/jci-2017-0010"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1015362185"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/database\/bat013"},{"key":"e_1_2_11_15_1","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab331","article-title":"High\u2010Dimensional Generalized Propensity Score With Application to Omics Data","volume":"22","author":"Gao Q.","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176325768"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c1ea43"},{"key":"e_1_2_11_18_1","volume-title":"Causal Inference","author":"Hern\u00e1n M. 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