Related Experiment Video
Updated: Jul 20, 2025
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
304
INES: Interactive tool for construction and extrapolation of partitioned survival models
Vicente Gimeno-Ballester1, Daniel Perez-Troncoso2, Antonio Olry-Labry3,4
1Servicio de Farmacia, Hospital Universitario Miguel Servet, Zaragoza, Spain.
Cost Effectiveness and Resource Allocation : C/E
|July 31, 2023
Summary
INES is an open-access R tool for partitioned survival models (PSM). It simplifies cost-effectiveness analysis by enabling rapid, flexible, and transparent parametric survival analysis for healthcare professionals.
Area of Science:
- Health Economics
- Biostatistics
- Survival Analysis
Background:
- Partitioned survival models (PSM) are crucial for economic evaluations in healthcare.
- Existing PSM implementation can be complex for non-statisticians.
- The INES (INteractive model for Extrapolation of Survival and cost) tool addresses this complexity.
Purpose of the Study:
- To describe the properties and suitability of the INES tool.
- To provide a user-friendly interface for PSM implementation.
- To facilitate economic evaluations in diverse healthcare contexts.
Main Methods:
- INES is an open-access R-powered tool utilizing a standalone Shiny app.
- It requires no prior R knowledge and operates via a batch file.
- User inputs include survival curves, costs, and hazard ratios from a template spreadsheet.
Main Results:
- The application integrates estimation of parametric hazard functions for survival analysis.
- It allows selection and application of these functions within a PSM framework.
- Calculations include mean costs and quality-adjusted life years (QALYs).
Conclusions:
- INES provides a rapid, flexible, robust, and transparent solution for parametric survival analysis.
- It simplifies the calculation of PSMs for cost-effectiveness evaluations.
- The tool is suitable for various applications in health economics and outcomes research.
Related Concept Videos
01:19Survival Tree
111
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
111
01:14Parametric Survival Analysis: Weibull and Exponential Methods
476
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...
476
01:15Assumptions of Survival Analysis
156
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.
156
01:18Introduction To Survival Analysis
282
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...
282
01:20Comparing the Survival Analysis of Two or More Groups
224
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...
224
01:21Cancer Survival Analysis
384
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...
384