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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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...
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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On small-sample inference in group randomized trials with binary outcomes and cluster-level covariates.

Philip M Westgate1

  • 1Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose Street, Lexington, KY 40536, USA. philip.westgate@uky.edu

Biometrical Journal. Biometrische Zeitschrift
|July 16, 2013
PubMed
Summary

We developed a pseudo-Wald statistic for group randomized trials (GRTs) with binary outcomes. This method, along with a cluster-level summary approach, improves statistical inference in GRTs.

Keywords:
Binary outcomesCluster-level summary approachMarginal modelModel-based standard errorSandwich standard error estimates

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Group randomized trials (GRTs) are essential for evaluating interventions at a group level.
  • Standard statistical methods may require adjustment for clustered data in GRTs.
  • Binary outcomes are common in health research, necessitating appropriate analytical techniques.

Purpose of the Study:

  • To develop and evaluate a pseudo-Wald statistic for improving inference in GRTs with binary outcomes.
  • To compare the performance of the proposed statistic against alternative methods, including bias-corrected sandwich estimators and cluster-level summaries.
  • To assess the accuracy of different statistical approaches in maintaining nominal test sizes under various GRT scenarios.

Main Methods:

  • Development of a pseudo-Wald statistic for marginal models with logistic links in GRTs.
  • Comparison with bias-corrected empirical sandwich standard error estimates.
  • Evaluation using cluster-level summary outcomes, including natural log of odds.
  • Simulation studies across diverse GRT settings to assess test size accuracy.

Main Results:

  • The pseudo-Wald statistic and a cluster-level summary using the natural log of odds demonstrated superior performance in maintaining nominal test sizes.
  • Some popular cluster-level summary approaches led to invalid inference due to weighting issues.
  • Bias-corrected empirical sandwich standard error estimates showed good performance, supporting the use of marginal models.

Conclusions:

  • The pseudo-Wald statistic offers an improved approach for inference in group randomized trials with binary outcomes.
  • Careful consideration of analytical methods is crucial, as some cluster-level approaches may yield invalid results.
  • Marginal models are applicable in GRT settings, particularly when enhanced with appropriate statistical techniques.