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What the DOI identifies

Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration

10.46243/jst.2025.v10.i11.pp01-10 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2025-11-21

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 11 · pp. 01

Principal agents

• Abubaker Bashir Ahmed Younis (author → Author)
• Altaiyb Omer Ahmed Mohmmed (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 17 references. Source: Crossref (member 25296), registered 2026-08-27, last deposited 2026-09-20.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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    "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. Conclusions: This reproducible worked example demonstrates that cause-specific Cox and Fine-Gray models provide complementary, estimand-specific descriptions of competing-risks data. Because the generator encoded zero covariate effects, the fitted coefficients are illustrative sample estimates and do not establish treatment effects, clinical associations, or real-world prostate cancer prognosis.",
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            "doi": "10.1002/9781118033005",
            "unstructured": "Lawless JF. Statistical Models and Methods for Lifetime Data. 2nd ed. Wiley; 2003. https://doi. org/10.1002/9781118033005"
        },
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            "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-"
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            "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.115.0 17719"
        },
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            "key": "ref5",
            "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/10.10 80/01621459.1999.10474144"
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            "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"
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            "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"
        },
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            "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"
        },
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            "key": "ref9",
            "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"
        },
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            "key": "ref10",
            "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"
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            "key": "ref11",
            "doi": "10.1016/j.eururo.2010.10.003",
            "unstructured": "Abdollah F, Sun M, Thuret R, et al. A competingrisks analysis of survival after alternative Abubaker Bashir Ahmed Younis, Altaiyb Omer Ahmed Mohmmed: Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration 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"
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            "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": "ref13",
            "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": "ref14",
            "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. tb0089 9.x"
        },
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            "doi": "10.1214/aos/1176344247",
            "unstructured": "Aalen OO. Nonparametric inference for a family of counting processes. Ann Stat. 1978;6(4):701-"
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            "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"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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