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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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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
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Group Design02:01

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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...
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Cluster Sampling Method01:20

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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...
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Sampling Plans01:23

Sampling Plans

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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.
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

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Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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Related Experiment Video

Updated: Apr 28, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Changing cluster composition in cluster randomised controlled trials: design and analysis considerations.

Neil Corrigan, Michael J G Bankart, Laura J Gray

  • 1Department of Health Sciences, University of Leicester, 22-28 Princess Road West, Leicester LE1 6TP, UK. karen.smith@leicester.ac.uk.

Trials
|June 3, 2014
PubMed
Summary

Cluster merging in randomised trials can reduce study power and introduce bias, especially with heterogeneous merges. Careful planning and analysis are needed to mitigate these effects in cluster randomised controlled trials.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Post-randomisation changes in cluster composition, specifically cluster merging, are under-addressed methodological challenges in cluster randomised controlled trials (CRCTs).
  • Understanding the impact of cluster merging on trial design, analysis, and interpretation is crucial for reliable outcomes.

Purpose of the Study:

  • To investigate the effects of cluster merging on study power and treatment effect estimates in CRCTs.
  • To differentiate the impacts of homogeneous and heterogeneous cluster merges on trial findings.

Main Methods:

  • Explored effects of cluster merging on study power using standard power calculation methods.
  • Assessed impacts of homogeneous and heterogeneous merges via simulation.
  • Applied standard analysis methods to evaluate bias and precision of treatment effect estimates.

Main Results:

  • Cluster merging systematically reduced study power, particularly with high cluster size variability.
  • Homogeneous merges had minimal analytical impact, with power changes offset by intracluster correlation coefficient (ICC) adjustments.
  • Heterogeneous merges decreased study power and attenuated treatment effect estimates, introducing bias.

Conclusions:

  • Previously reported CRCTs predominantly featured homogeneous cluster merges, which simulations suggest yield unbiased findings.
  • Heterogeneous cluster merges introduce quantifiable bias and reduce estimate precision, necessitating methodological advancements for appropriate analysis.
  • Recommendations include avoiding merges, discontinuing clusters after heterogeneous merges, and adjusting sample size calculations for cluster size variability and ICC.