{ "ok": true, "doi": "10.46243/jst.2026.v11.i05.pp01-19", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:32", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "Background: Competing mortality complicates estimation and interpretation of prostate cancer-specific death. Cause-specific Cox and Fine-Gray models answer related but different questions and should be selected according to the target estimand. Objective: To demonstrate, using a fully reproducible fixed synthetic dataset, how cause-specific and subdistribution hazard estimands differ in risk-set construction, regression interpretation, and relation to cumulative incidence. Methods: A single fixed dataset of 500 synthetic observations was generated using seed 123. Covariates, treatment indicators, follow-up time sampled from 1-60 months, and event status sampled with probabilities 0.20 for prostate cancer death, 0.20 for other-cause death, and 0.60 for censoring were mutually independent; therefore, no covariate or treatment effects were encoded. Multivariable cause-specific Cox and Fine-Gray models included 15 regression parameters. Cumulative incidence functions, proportionality diagnostics, event-per-parameter calculations, an exploratory other-cause Cox model, and comparison with the naive Kaplan-Meier complement were examined. Results: Within this single realization, the fitted hormonal-therapy estimates were below unity in both models (CSHR 0.568, 95% CI 0.381-0.849; SHR 0.605, 95% CI 0.405-0.903). Stage III versus stage I had estimates in the same direction and of comparable magnitude (CSHR 2.227, 95% CI 1.045-4.746; SHR 2.006, 95% CI 0.948-4.245), and the difference in p-value thresholds was not interpreted as model disagreement. Global proportionality tests were not significant for the primary cause-specific Cox (p = 0.605) or Fine-Gray (p = 0.550) model. 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J R Stat Soc Series B Stat Methodol. 1972;34(2):187-220. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x" }, { "path": "references.13.doi", "from": null, "to": "10.1214/aos/1176344247" }, { "path": "references.13.key", "from": null, "to": "ref14" }, { "path": "references.13.unstructured", "from": null, "to": "Aalen OO. Nonparametric inference for a family of counting processes. Ann Stat. 1978;6(4):701-726. https://doi.org/10.1214/aos/1176344247" }, { "path": "references.14.doi", "from": null, "to": "10.1111/j.1541-0420.2010.01420.x" }, { "path": "references.14.key", "from": null, "to": "ref15" }, { "path": "references.14.unstructured", "from": null, "to": "Geskus RB. Cause-specific cumulative incidence estimation and the Fine and Gray model under both left truncation and right censoring. 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J Am Stat Assoc. 1999;94(446):496-509. https://doi.org/10.1080/01621459.1999.10474144" }, { "path": "references.4.doi", "from": null, "to": "10.1002/sim.2712" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26(11):2389-2430. https://doi.org/10.1002/sim.2712" }, { "path": "references.5.doi", "from": null, "to": "10.1158/1078-0432.ccr-11-2097" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "Dignam JJ, Zhang Q, Kocherginsky M. The use and interpretation of competing risks regression models. Clin Cancer Res. 2012;18(8):2301-2308. https://doi.org/10.1158/1078-0432.CCR-11-2097" }, { "path": "references.6.doi", "from": null, "to": "10.1002/sim.7501" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "Austin PC, Fine JP. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med. 2017;36(27):4391-4400. https://doi.org/10.1002/sim.7501" }, { "path": "references.7.doi", "from": null, "to": "10.1093/eurheartj/ehu131" }, { "path": "references.7.key", "from": null, "to": "ref8" }, { "path": "references.7.unstructured", "from": null, "to": "Wolbers M, Koller MT, Stel VS, et al. Competing risks analyses: objectives and approaches. 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Eur Urol. 2011;59(1):88-95. https://doi.org/10.1016/j.eururo.2010.10.003" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2026.v11.i05.pp01-19", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2026.v11.i05.pp01-19" } ], "agents": [ { "role": "author", "name": { "given": "Abubaker", "family": "Bashir Ahmed Younis" }, "sequence": "first" }, { "role": "author", "name": { "given": "Altaiyb", "family": "Omer Ahmed Mohmmed" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2026-05-22", "date_type": "PublicationDate", "online": "2026-05-22" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "11", "issue": "05", "pages": { "first": "1" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/1553", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1553/1127", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1553/1160", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/index.php/pub/article/view/1553/1127", "purpose": "similarity-checking" } ], "abstract": { "value": "Background: Competing mortality complicates estimation and interpretation of prostate cancer-specific death. Cause-specific Cox and Fine-Gray models answer related but different questions and should be selected according to the target estimand. Objective: To demonstrate, using a fully reproducible fixed synthetic dataset, how cause-specific and subdistribution hazard estimands differ in risk-set construction, regression interpretation, and relation to cumulative incidence. Methods: A single fixed dataset of 500 synthetic observations was generated using seed 123. Covariates, treatment indicators, follow-up time sampled from 1-60 months, and event status sampled with probabilities 0.20 for prostate cancer death, 0.20 for other-cause death, and 0.60 for censoring were mutually independent; therefore, no covariate or treatment effects were encoded. Multivariable cause-specific Cox and Fine-Gray models included 15 regression parameters. Cumulative incidence functions, proportionality diagnostics, event-per-parameter calculations, an exploratory other-cause Cox model, and comparison with the naive Kaplan-Meier complement were examined. Results: Within this single realization, the fitted hormonal-therapy estimates were below unity in both models (CSHR 0.568, 95% CI 0.381-0.849; SHR 0.605, 95% CI 0.405-0.903). Stage III versus stage I had estimates in the same direction and of comparable magnitude (CSHR 2.227, 95% CI 1.045-4.746; SHR 2.006, 95% CI 0.948-4.245), and the difference in p-value thresholds was not interpreted as model disagreement. Global proportionality tests were not significant for the primary cause-specific Cox (p = 0.605) or Fine-Gray (p = 0.550) model. At 60 months, the naive Kaplan-Meier complement exceeded the cumulative incidence estimate by 12.2 percentage points, although only seven observations remained at risk.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2026-05-22", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1002/9781118033005", "unstructured": "Lawless JF. Statistical Models and Methods for Lifetime Data. 2nd ed. Wiley; 2003. https://doi.org/10.1002/9781118033005" }, { "key": "ref2", "doi": "10.1093/ije/dyr213", "unstructured": "Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41(3):861-870. https://doi.org/10.1093/ije/dyr213" }, { "key": "ref3", "doi": "10.1161/circulationaha.115.017719", "unstructured": "Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133(6):601-609. https://doi.org/10.1161/CIRCULATIONAHA.115.017719" }, { "key": "ref4", "doi": "10.1080/01621459.1999.10474144", "unstructured": "Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496-509. https://doi.org/10.1080/01621459.1999.10474144" }, { "key": "ref5", "doi": "10.1002/sim.2712", "unstructured": "Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26(11):2389-2430. https://doi.org/10.1002/sim.2712" }, { "key": "ref6", "doi": "10.1158/1078-0432.ccr-11-2097", "unstructured": "Dignam JJ, Zhang Q, Kocherginsky M. The use and interpretation of competing risks regression models. Clin Cancer Res. 2012;18(8):2301-2308. https://doi.org/10.1158/1078-0432.CCR-11-2097" }, { "key": "ref7", "doi": "10.1002/sim.7501", "unstructured": "Austin PC, Fine JP. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med. 2017;36(27):4391-4400. https://doi.org/10.1002/sim.7501" }, { "key": "ref8", "doi": "10.1093/eurheartj/ehu131", "unstructured": "Wolbers M, Koller MT, Stel VS, et al. Competing risks analyses: objectives and approaches. Eur Heart J. 2014;35(42):2936-2941. https://doi.org/10.1093/eurheartj/ehu131" }, { "key": "ref9", "doi": "10.1002/sim.5459", "unstructured": "Gerds TA, Scheike TH, Andersen PK. Absolute risk regression for competing risks: interpretation, link functions, and prediction. Stat Med. 2012;31(29):3921-3930. https://doi.org/10.1002/sim.5459" }, { "key": "ref10", "doi": "10.1016/j.eururo.2010.10.003", "unstructured": "Abdollah F, Sun M, Thuret R, et al. A competing-risks analysis of survival after alternative treatment modalities for prostate cancer patients: 1988-2006. Eur Urol. 2011;59(1):88-95. https://doi.org/10.1016/j.eururo.2010.10.003" }, { "key": "ref11", "doi": "10.1038/sj.bjc.6602102", "unstructured": "Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks in survival data analysis. Br J Cancer. 2004;91(7):1229-1235. https://doi.org/10.1038/sj.bjc.6602102" }, { "key": "ref12", "doi": "10.1002/sim.9023", "unstructured": "Austin PC, Steyerberg EW, Putter H. Fine-Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: cumulative total failure probability may exceed 1. Stat Med. 2021;40(19):4200-4212. https://doi.org/10.1002/sim.9023" }, { "key": "ref13", "doi": "10.1111/j.2517-6161.1972.tb00899.x", "unstructured": "Cox DR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol. 1972;34(2):187-220. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x" }, { "key": "ref14", "doi": "10.1214/aos/1176344247", "unstructured": "Aalen OO. Nonparametric inference for a family of counting processes. Ann Stat. 1978;6(4):701-726. https://doi.org/10.1214/aos/1176344247" }, { "key": "ref15", "doi": "10.1111/j.1541-0420.2010.01420.x", "unstructured": "Geskus RB. Cause-specific cumulative incidence estimation and the Fine and Gray model under both left truncation and right censoring. Biometrics. 2011;67(1):39-49. https://doi.org/10.1111/j.1541- 0420.2010.01420.x" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-08-26", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/1553", "sha256_before": null, "sha256_after": "21bb8e697ee99ab7e986d16b8296f6b353066758bd76947c75f293d91fdac79f" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:51", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2026.v11.i05.pp01-19", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2026.v11.i05.pp01-19" } ], "agents": [ { "role": "author", "name": { "given": "Abubaker", "family": "Bashir Ahmed Younis" }, "sequence": "first" }, { "role": "author", "name": { "given": "Altaiyb", "family": "Omer Ahmed Mohmmed" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2026-05-22", "date_type": "PublicationDate", "online": "2026-05-22" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "11", "issue": "05", "pages": { "first": "1" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/1553", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1553/1127", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1553/1160", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/index.php/pub/article/view/1553/1127", "purpose": "similarity-checking" } ], "abstract": { "value": "Background: Competing mortality complicates estimation and interpretation of prostate cancer-specific death. Cause-specific Cox and Fine-Gray models answer related but different questions and should be selected according to the target estimand. Objective: To demonstrate, using a fully reproducible fixed synthetic dataset, how cause-specific and subdistribution hazard estimands differ in risk-set construction, regression interpretation, and relation to cumulative incidence. Methods: A single fixed dataset of 500 synthetic observations was generated using seed 123. Covariates, treatment indicators, follow-up time sampled from 1-60 months, and event status sampled with probabilities 0.20 for prostate cancer death, 0.20 for other-cause death, and 0.60 for censoring were mutually independent; therefore, no covariate or treatment effects were encoded. Multivariable cause-specific Cox and Fine-Gray models included 15 regression parameters. Cumulative incidence functions, proportionality diagnostics, event-per-parameter calculations, an exploratory other-cause Cox model, and comparison with the naive Kaplan-Meier complement were examined. Results: Within this single realization, the fitted hormonal-therapy estimates were below unity in both models (CSHR 0.568, 95% CI 0.381-0.849; SHR 0.605, 95% CI 0.405-0.903). Stage III versus stage I had estimates in the same direction and of comparable magnitude (CSHR 2.227, 95% CI 1.045-4.746; SHR 2.006, 95% CI 0.948-4.245), and the difference in p-value thresholds was not interpreted as model disagreement. Global proportionality tests were not significant for the primary cause-specific Cox (p = 0.605) or Fine-Gray (p = 0.550) model. 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