Related Experiment Video
Updated: Jul 8, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Robust covariate-adjusted log-rank statistics and corresponding sample size formula for recurrent events data
Rui Song1, Michael R Kosorok1, Jianwen Cai1
1Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599-7420, U.S.A.
Biometrics
|December 29, 2007
Summary
This study introduces robust log-rank tests for recurrent events data, enhancing clinical trial analysis with covariate adjustment. The developed sample size formula improves the accuracy of planning studies with multiple events per subject.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Survival Analysis
Background:
- Recurrent events data are common in clinical trials, posing analytical challenges.
- Existing methods may not adequately handle multiple events per subject or covariate adjustments.
- Robust statistical methods are needed for accurate analysis of such data.
Purpose of the Study:
- To develop robust covariate-adjusted log-rank statistics for recurrent events data.
- To derive a corresponding sample size formula for clinical trial planning.
- To provide a method that is robust to various data-generating processes.
Main Methods:
- Development of covariate-adjusted log-rank statistics for recurrent events.
- Derivation of a sample size formula based on asymptotic normality.
- Application to data from an rhDNase study for illustration.
- Simulations to assess type I error control and power.
Main Results:
- The proposed log-rank tests are robust and adjusted for predictive covariates.
- The sample size formula is applicable under specific assumptions and reduces to known forms in simpler cases.
- Simulations demonstrate good type I error control and power comparisons.
Conclusions:
- The developed methods offer a robust approach for analyzing recurrent events data in clinical trials.
- The sample size formula aids in efficient study design for recurrent event endpoints.
- The methodology is validated through simulations and a real-world study example.
Related Concept Videos
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.
01:20Comparing 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...
01:14Parametric Survival Analysis: Weibull and Exponential Methods
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...
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
01:21Friedman Two-way Analysis of Variance by Ranks
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
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.