Related Experiment Video
Updated: May 9, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Testing the proportional hazards assumption in case-cohort analysis
Xiaonan Xue1, Xianhong Xie, Marc Gunter
1Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA. xiaonan.xue@einstein.yu.edu
BMC Medical Research Methodology
|July 10, 2013
Summary
New correlation tests accurately assess proportional hazards in case-cohort Cox models, crucial for reliable epidemiological research on rare diseases. This ensures valid analysis and prevents erroneous scientific findings.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Case-cohort studies are widely used for rare disease research.
- Cox regression models are the primary analysis method.
- No established methods exist to test proportional hazards assumptions in case-cohort Cox models.
Purpose of the Study:
- To develop and validate methods for assessing the proportional hazards assumption in case-cohort Cox models.
- To ensure the reliability of epidemiological findings derived from case-cohort studies.
Main Methods:
- Extended Schoenfeld residuals correlation test for case-cohort data.
- Utilized pseudolikelihood functions to define "case-cohort Schoenfeld residuals".
- Correlated residuals with event time, rank order, and Kaplan-Meier estimates; validated via simulations and a colorectal cancer study.
Main Results:
- Simulation studies confirmed the accuracy of the three proposed correlation tests in detecting non-proportionality.
- The tests identified violations of the proportional hazards assumption for specific exposure variables in a colorectal cancer case-cohort study.
- Alternative analytical methods were successfully employed to address identified assumption violations.
Conclusions:
- The proposed correlation tests offer a straightforward and effective approach for evaluating the proportional hazards assumption in case-cohort Cox analyses.
- Validating this assumption is critical to prevent the publication of potentially erroneous scientific conclusions due to unrecognized model violations.
Related Concept Videos
01:15Assumptions of Survival Analysis
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.
01:20Comparing the Survival Analysis of Two or More Groups
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 Cox...
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
01:24Testing a Claim about Population Proportion
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:11Hazard Rate
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
01:18Introduction To Survival Analysis
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 until a...
The primary goal of survival analysis is to estimate survival time—the time until a...