Related Experiment Video
Updated: Mar 27, 2026

04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
11.0K
Partner status and survival after cancer: A competing risks analysis
Paramita Dasgupta1, Gavin Turrell2, Joanne F Aitken3
1Cancer Council Queensland, P.O. Box 201, Spring Hill, QLD 4004, Australia.
Cancer Epidemiology
|January 18, 2016
Summary
Cancer patients without a partner face significantly higher mortality risks from both cancer and other causes. This highlights the importance of social support in cancer survival and management.
Area of Science:
- Oncology
- Epidemiology
- Public Health
Background:
- The survival advantage associated with having a partner in cancer patients is recognized for all cancers combined.
- However, the prognostic impact of partner status on individual cancer types and competing mortality causes remains less understood.
Purpose of the Study:
- To quantify the impact of partner status on cancer-specific survival.
- To assess the influence of partner status on competing mortality causes.
Main Methods:
- Utilized population-based data from the Queensland Cancer Registry (1996-2012).
- Included 176,050 incident cases across ten leading cancer types.
- Employed flexible parametric competing-risks models, adjusting for age and stage, and stratifying by sex.
Main Results:
- Unpartnered individuals (males and females) exhibited a higher cumulative probability of death across all cancer sites compared to partnered individuals.
- Patients without a partner demonstrated increased mortality risk, with specific effects varying by cancer site, sex, and cause of death.
- For all sites combined, unpartnered males showed a 46% higher risk of cancer-specific mortality, 18% higher risk of other cancer mortality, and 44% higher risk of non-cancer mortality, with similar patterns observed in females. These risks persisted after adjusting for cancer stage.
Conclusions:
- Partner status significantly influences cancer patient survival, affecting both cancer-specific and competing mortality.
- Understanding the mechanisms behind the survival benefits of having a partner is crucial.
- This knowledge can inform improved cancer management strategies to support all cancer patients.
Related Concept Videos
Cancer Survival Analysis
823
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
823
Comparing the Survival Analysis of Two or More Groups
703
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...
703
Assumptions of Survival Analysis
486
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.
486
Kaplan-Meier Approach
715
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
715
Introduction To Survival Analysis
955
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
955
Actuarial Approach
359
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
359

