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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Quantifying Suicide Risk in Prostate Cancer: A SEER-Based Predictive Model.

Jiaxing Du1, Fen Zhang1, Weinan Zheng1

  • 1Department of Pediatric Intensive Care Unit, Dongguan Children's Hospital Affiliated to Guangdong Medical University, Dongguan, China.

Journal of Epidemiology and Global Health
|March 20, 2025
PubMed
Summary

Prostate cancer patients face a higher suicide risk. A new nomogram, developed using SEER data, effectively identifies high-risk individuals for targeted interventions.

Keywords:
NomogramProstate cancerRisk predictionSEERSuicide

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Area of Science:

  • Oncology
  • Public Health
  • Biostatistics

Background:

  • Prostate cancer is linked to increased suicide risk compared to the general population.
  • Effective intervention strategies require accurate identification of high-risk patients.

Purpose of the Study:

  • To develop a nomogram for quantifying suicide risk in prostate cancer patients.
  • To provide empirical evidence for guiding suicide prevention interventions.

Main Methods:

  • Analysis of 176,730 prostate cancer patients from the SEER database (2004-2021).
  • Development of a nomogram using LASSO feature selection and Cox regression.
  • Model validation through internal validation, C-index, and time-dependent ROC curves.

Main Results:

  • Seven independent predictors of suicide were identified.
  • The nomogram showed favorable discriminative capability (C-index 0.746 training, 0.703 validation).
  • Calibration plots and Kaplan-Meier analysis confirmed the model's accuracy and discriminative ability.

Conclusions:

  • An applicable nomogram was developed for individualized suicide risk quantification in prostate cancer patients.
  • This tool aids clinicians in identifying high-risk individuals for timely interventions.
  • Limitations include lack of detailed clinical/mental health data and potential biases.