Related Experiment Video
Updated: Aug 30, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.3K
Standardised survival probabilities: a useful and informative tool for reporting regression models for survival data
Elisavet Syriopoulou1, Tove Wästerlid2,3, Paul C Lambert4,5
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. elisavet.syriopoulou@ki.se.
British Journal of Cancer
|September 1, 2022
Summary
Standardised survival probabilities offer a clearer way to understand treatment effects in time-to-event studies. This method improves upon hazard ratios by providing an interpretable measure of risk, even with confounding factors present.
Area of Science:
- Biostatistics
- Epidemiology
- Clinical Research
Background:
- Kaplan-Meier estimator and regression models are common for time-to-event outcomes.
- Hazard ratios are frequently misinterpreted as relative risks, leading to confusion.
- Confounding factors can complicate the interpretation of treatment effects.
Purpose of the Study:
- To introduce and evaluate standardised survival probabilities as an alternative to hazard ratios.
- To demonstrate a method for summarizing regression model analyses with improved interpretability.
- To provide a more accurate measure of treatment effect in the presence of confounding.
Main Methods:
- Utilized standardised survival probabilities derived from regression models.
- Calculated survival probabilities for hypothetical scenarios of universal treatment and no treatment.
- Applied the method to publicly available breast cancer data.
Main Results:
- Standardised survival probabilities effectively compared treatment effects in breast cancer data after adjusting for confounding.
- Demonstrated the utility of standardisation for subgroup analyses.
- Showcased the informative interpretation of standardised survival probabilities in terms of risk.
Conclusions:
- Standardised survival probabilities are a valuable tool for reporting treatment effects.
- This method enhances clarity by adjusting for confounding and offering direct risk interpretation.
- Offers a superior alternative to hazard ratios for communicating time-to-event outcomes.
Related Concept Videos
01:20Comparing the Survival Analysis of Two or More Groups
261
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...
261
01:21Cancer Survival Analysis
429
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...
429
01:18Introduction To Survival Analysis
355
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...
355
01:15Assumptions of Survival Analysis
176
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.
176
01:14Parametric Survival Analysis: Weibull and Exponential Methods
556
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
556
01:18Survival Curves
267
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
267