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Related Concept Videos

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.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Study Design in Statistics01:15

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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,
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Randomized Experiments01:13

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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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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Sample size calculation for multicenter randomized trial: taking the center effect into account.

Emilie Vierron1, Bruno Giraudeau

  • 1INSERM CIC 202, Université François Rabelais, Tours, CHRU de Tours, France. emilie.vierron@med.univ-tours.fr

Contemporary Clinical Trials
|December 26, 2006
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Summary
This summary is machine-generated.

This study introduces a new sample size formula for multicenter trials that accounts for the center effect, reducing required sample sizes. The formula adjusts calculations based on the intraclass correlation coefficient (ICC) for more efficient trial design.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Research

Background:

  • Multicenter trials exhibit data similarity within centers, termed the center effect.
  • The intraclass correlation coefficient (ICC) quantifies this center effect.
  • Standard sample size calculations often overlook the center effect, potentially leading to inefficiencies.

Purpose of the Study:

  • To derive a novel sample size formula for continuous data in multicenter trials.
  • To incorporate the center effect, quantified by the ICC, into sample size determination.
  • To provide a method for adjusting and potentially reducing sample sizes in multicenter studies.

Main Methods:

  • Analytical derivation of a sample size formula for continuous data.
  • Inclusion of the intraclass correlation coefficient (rho) as a key factor in the formula.
  • Comparison of the new formula with the classical approach.

Main Results:

  • A new, elementary sample size formula was derived.
  • The formula incorporates a (1-rho) factor, directly accounting for the ICC.
  • The derived formula allows for sample size reduction based on the magnitude of the center effect.

Conclusions:

  • The proposed formula enables more accurate and efficient sample size calculations in multicenter trials.
  • Adjusting for the center effect leads to reduced sample size requirements.
  • This approach enhances the consistency and efficiency of conducting multicenter randomized trials.