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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...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

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Related Experiment Video

Updated: Jul 2, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A simulation based technique to estimate intracluster correlation for a binary variable.

Hrishikesh Chakraborty1, Janet Moore, Waldemar A Carlo

  • 1Statistics and Epidemiology Division, RTI International, Research Triangle Park, North Carolina 27709-2194, USA. hchakraborty@rti.org

Contemporary Clinical Trials
|August 30, 2008
PubMed
Summary
This summary is machine-generated.

Estimating the intracluster correlation (ICC) is crucial for designing effective cluster randomized trials. This study introduces a simulation technique to estimate ICC values for binary outcomes, aiding in sample size calculations for public health interventions.

Related Experiment Videos

Last Updated: Jul 2, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cluster randomized trials (CRTs) are widely used for health intervention evaluations in community settings.
  • Accurate sample size determination in CRTs relies heavily on estimates of cluster size and intracluster correlation (ICC).
  • Investigators often possess preliminary data on outcome ranges and cluster sizes during trial design.

Purpose of the Study:

  • To present a simulation technique for estimating the ICC and its distribution for binary outcomes in CRTs.
  • To provide a method for assessing the impact of varying cluster numbers and sizes on ICC estimates.
  • To aid in designing appropriately powered CRTs by estimating potential ICC ranges.

Main Methods:

  • A simulation technique was developed to estimate ICC values and their distributions.
  • The method accommodates known binary outcome variables with varying numbers of clusters and cluster sizes.
  • The technique was applied to a multi-country trial on neonatal resuscitation and mortality.

Main Results:

  • The simulation technique provides estimates of ICC values and confidence intervals.
  • It allows for the exploration of the distribution of ICC across different trial parameters.
  • Application to a neonatal mortality trial demonstrated its utility in a real-world setting.

Conclusions:

  • The proposed simulation technique is valuable for estimating ICC in CRTs with binary outcomes.
  • It assists researchers in understanding the potential range of ICC values.
  • This method supports the design of adequately powered CRTs for public health research.