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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.
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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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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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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Distributions to Estimate Population Parameter01:26

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Sampling Distribution01:12

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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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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Related Experiment Video

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A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method.

Danila Azzolina1,2, Paola Berchialla3, Silvia Bressan4

  • 1Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35122 Padua, Italy.

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|November 11, 2022
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Summary

This study presents a flexible Bayesian method for estimating sample sizes in clinical trials with binary outcomes. It offers an alternative to frequentist designs, particularly for small sample sizes, by incorporating prior knowledge.

Keywords:
Bayesian trialelicitationphase IIsample sizesemiparametric

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Inference

Background:

  • Sample size estimation is critical in clinical trials, with binomial experiments being common.
  • Frequentist methods are standard, but Bayesian approaches offer advantages, especially for small sample sizes.
  • Bayesian methods incorporate prior knowledge into a prior distribution, accounting for data uncertainty.

Purpose of the Study:

  • To propose a flexible Bayesian approach for binomial sample size estimation.
  • To generalize existing criteria like average length, coverage, and worst outcome.
  • To explore both parametric (Beta priors) and semiparametric (B-splines) prior distributions.

Main Methods:

  • Investigated generalized versions of average length, coverage, and worst outcome criteria.
  • Extended Joseph's Beta-Binomial model for sample size estimation.
  • Utilized parametric (Beta) and semiparametric (B-spline) priors for enhanced flexibility.

Main Results:

  • Developed a generalized framework for Bayesian sample size estimation in binomial settings.
  • Demonstrated the flexibility of incorporating expert opinion through prior elicitation.
  • Showcased the utility of both parametric and semiparametric priors for improved sample size determination.

Conclusions:

  • The proposed Bayesian method offers a more flexible alternative for sample size estimation in clinical trials with binary data.
  • This approach effectively incorporates prior information and expert opinion, enhancing study design.
  • The use of semiparametric priors provides greater adaptability compared to purely parametric models.