Cite this DOI

Ahmed Younis, A. B., & Ahmed Mohmmed, A. O. (2025). Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration. *Journal of Science & Technology*, *10*(11), 01. https://doi.org/10.46243/jst.2025.v10.i11.pp01-10

@article{ahmedyounis2025causespecific,
  author    = {Ahmed Younis, Abubaker Bashir and Ahmed Mohmmed, Altaiyb Omer},
  title     = {{Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {nov},
  volume    = {10},
  number    = {11},
  pages     = {01},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i11.pp01-10},
  url       = {https://doi.org/10.46243/jst.2025.v10.i11.pp01-10},
  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. 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.}
}

TY  - JOUR
TI  - Cause-Specific Cox and Fine-Gray Regression in Competing Risks: A Reproducible Synthetic Prostate Cancer Illustration
AU  - Ahmed Younis, Abubaker Bashir
AU  - Ahmed Mohmmed, Altaiyb Omer
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/11/21/
VL  - 10
IS  - 11
SP  - 01
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. 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.
DO  - 10.46243/jst.2025.v10.i11.pp01-10
UR  - https://doi.org/10.46243/jst.2025.v10.i11.pp01-10
ER  -

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    "DOI": "10.46243/jst.2025.v10.i11.pp01-10",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i11.pp01-10",
    "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": "Ahmed Younis",
            "given": "Abubaker Bashir"
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        {
            "family": "Ahmed Mohmmed",
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                2025,
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    "page": "01",
    "publisher": "Longman Publishers",
    "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. 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.",
    "ISSN": "2456-5660"
}

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.2025.v10.i11.pp01-10 gives all four in one JSON answer.