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Related Concept Videos

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

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

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...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
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Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

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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Study Design in Statistics

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Longitudinal Studies

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Related Experiment Videos

Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome.

Jingxia Liu1,2, Fan Li3,4, Xuping Luo2

  • 1Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA.

Biometrical Journal. Biometrische Zeitschrift
|May 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for optimizing sample size in open-cohort longitudinal cluster randomized trials (LCRTs). The methods maximize design efficiency by considering cost and correlation parameters for improved trial planning.

Keywords:
MaxiMin optimal design (MMD)cluster randomized crossover (CRXO) triallocal optimal design (LOD)parallel‐arm longitudinal cluster randomized trial (PA‐LCRT)relative efficiency (RE)stepped wedge cluster randomized trial (SW‐CRT)

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Existing methods for longitudinal cluster randomized trials (LCRTs) do not fully optimize open-cohort designs for efficiency.
  • Sample size calculations for open-cohort LCRTs with constant churn rates have been previously addressed, but not optimal designs maximizing efficiency.

Purpose of the Study:

  • To develop algorithms for optimal sample size determination in open-cohort LCRTs, maximizing design efficiency under a cost-efficiency framework.
  • To propose Local Optimal Design (LOD) and MaxiMin optimal design strategies for varying correlation parameter knowledge.

Main Methods:

  • Development of algorithms for optimal sample size calculation in open-cohort LCRTs with a fixed number of periods and constant replacement individuals.
  • Application of cost-efficiency frameworks to derive optimal cluster-period size, number of clusters, and power.
  • Comparison of optimal designs under known and unknown correlation parameters for different LCRT variants (PA-LCRTs, CRXO, SW-CRTs).

Main Results:

  • For known correlation parameters, optimal cluster-period size in PA-LCRTs generally decreases then increases with more replacements, while cluster number and power decrease then increase.
  • In contrast, for CRXO and SW-CRTs, optimal cluster-period size and churn rate increase, while cluster number and power decrease with more replacements.
  • When correlation parameters are unknown, PA-LCRTs and CRXO trials show similar optimal designs with few replacements, with replacement numbers having less impact on cluster-period size than cluster number.

Conclusions:

  • The proposed algorithms provide a framework for optimizing sample size and design efficiency in open-cohort LCRTs.
  • The findings highlight how the number of replaced individuals impacts optimal design choices differently across various LCRT structures.
  • These methods offer practical tools for real-world LCRT planning, demonstrated through two case studies.