Cite this DOI

Bashir Ahmed Younis, A., & Omer Ahmed Mohmmed, A. (2026). Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration. *Journal of Science & Technology*, *11*(05), 1. https://doi.org/10.46243/jst.2026.v11.i05.pp01-19

@article{bashirahmedyounis2026causespecific,
  author    = {Bashir Ahmed Younis, Abubaker and Omer Ahmed Mohmmed, Altaiyb},
  title     = {{Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration}},
  journal   = {Journal of Science \& Technology},
  year      = {2026},
  month     = {may},
  volume    = {11},
  number    = {05},
  pages     = {1},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2026.v11.i05.pp01-19},
  url       = {https://doi.org/10.46243/jst.2026.v11.i05.pp01-19},
  language  = {en},
  abstract  = {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.}
}

TY  - JOUR
TI  - Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration
AU  - Bashir Ahmed Younis, Abubaker
AU  - Omer Ahmed Mohmmed, Altaiyb
JO  - Journal of Science & Technology
PY  - 2026
DA  - 2026/05/22/
VL  - 11
IS  - 05
SP  - 1
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2026.v11.i05.pp01-19
UR  - https://doi.org/10.46243/jst.2026.v11.i05.pp01-19
ER  -

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    "DOI": "10.46243/jst.2026.v11.i05.pp01-19",
    "URL": "https://doi.org/10.46243/jst.2026.v11.i05.pp01-19",
    "title": "Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Bashir Ahmed Younis",
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            "family": "Omer Ahmed Mohmmed",
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        "date-parts": [
            [
                2026,
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    "abstract": "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.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2026.v11.i05.pp01-19 gives all four in one JSON answer.