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Related Concept Videos

Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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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

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Body: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...
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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
Simple...
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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

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Body: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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Odds Ratio01:09

Odds Ratio

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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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Related Experiment Video

Updated: Dec 25, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Sample size calculation in randomised phase II selection trials using a margin of practical equivalence.

Hakim-Moulay Dehbi1, Allan Hackshaw2

  • 1Comprehensive Clinical Trials Unit at University College London (UCL), 90 High Holborn, London, WC1V 6LJ, UK. h.dehbi@ucl.ac.uk.

Trials
|April 2, 2020
PubMed
Summary

This study introduces a flexible randomized trial design for rare cancers, allowing selection of treatments based on efficacy, toxicity, or cost. A new web tool calculates sample sizes for this design, improving upon the

Keywords:
Randomised trialRare cancersSelection design

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology

Background:

  • Rare cancers and subtypes often require comparing multiple promising treatments.
  • Traditional 'pick-the-winner' designs prioritize efficacy but may overlook other crucial factors.
  • Alternative treatment selection criteria include toxicity, quality of life, and cost.

Purpose of the Study:

  • To introduce a flexible randomized selection trial design incorporating a margin of practical equivalence.
  • To provide a method for calculating sample sizes for such trials.
  • To offer a user-friendly web application for sample size calculations.

Main Methods:

  • Utilized exact binomial probabilities to calculate sample sizes for two- and three-arm randomized trials.
  • Incorporated a margin of practical equivalence to assess treatment differences and similarities.
  • Developed a web application for calculating required sample sizes based on various input parameters.

Main Results:

  • The margin of practical equivalence allows for a more nuanced assessment of treatment effects.
  • A free, user-friendly web application is available for sample size calculations.
  • The application supports various input parameters for diverse trial scenarios.

Conclusions:

  • The proposed randomized selection design with a margin of practical equivalence offers greater flexibility than 'pick-the-winner' designs.
  • This approach facilitates more comprehensive treatment assessment by considering factors beyond just efficacy.
  • The web application is expected to promote the adoption of this enhanced trial design.