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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing 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...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Survival Tree01:19

Survival Tree

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 survival tree begins...
Introduction To Survival Analysis01:18

Introduction 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...

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Related Experiment Video

Updated: Jun 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling

Xianghua Luo1, Chiung-Yu Huang

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, U.S.A. luox0054@umn.edu

Statistics in Medicine
|October 22, 2010
PubMed
Summary

This study introduces novel methods for analyzing recurrent event gap times in medical research. These techniques, including weighted risk-set (WRS) and modified within-cluster resampling (MWCR), accurately model sequential data.

Related Experiment Videos

Last Updated: Jun 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Statistics

Background:

  • Recurrent event gap times are crucial in medical and epidemiological studies.
  • Analyzing these gap times requires specialized methods due to their sequential nature, differing from standard clustered survival data.
  • Existing methods may not adequately capture the complexities of recurrent event data.

Purpose of the Study:

  • To extend existing risk-set based methods for analyzing univariate survival data to recurrent gap times.
  • To introduce novel building blocks: the averaged counting process and averaged at-risk process.
  • To propose a practical modified within-cluster resampling (MWCR) method for recurrent event analysis.

Main Methods:

  • Development of weighted risk-set (WRS) estimation methods using averaged counting and at-risk processes.
  • Extension of univariate survival analysis techniques to recurrent gap times.
  • Introduction and implementation of a modified within-cluster resampling (MWCR) method.

Main Results:

  • The averaged counting process and averaged at-risk process enable the extension of risk-set based methods to recurrent gap times.
  • The proposed MWCR method is shown to be asymptotically equivalent to the WRS estimators.
  • Demonstrated the utility of the methods with an analysis of Danish Psychiatric Central Register hospitalization data.

Conclusions:

  • The proposed WRS and MWCR methods provide effective and adaptable tools for analyzing recurrent event gap times.
  • These methods facilitate the accurate modeling of sequential data in medical and epidemiological research.
  • The MWCR method offers a practical approach for implementation in standard statistical software.