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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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Bootstrapping01:24

Bootstrapping

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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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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Sample size formulae for the Bayesian continual reassessment method.

Ying Kuen Cheung1

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA.

Clinical Trials (London, England)
|August 23, 2013
PubMed
Summary
This summary is machine-generated.

New formulae provide quick and accurate sample size calculations for dose finding studies using the Bayesian continual reassessment method (CRM). This aids in planning clinical trials and determining the maximum tolerated dose with high accuracy.

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Area of Science:

  • * Clinical Trial Design
  • * Biostatistics
  • * Pharmaceutical Research

Background:

  • * Accurate selection of the maximum tolerated dose is crucial in dose-finding study planning.
  • * Existing dose-finding methods lack concrete guidance on sample size determination.
  • * Sample size is a critical factor in the efficiency and success of clinical trials.

Purpose of the Study:

  • * To present closed-form formulae for sample size determination specifically for the Bayesian continual reassessment method (CRM).
  • * To offer quick and easy calculations for trial planners.
  • * To address the gap in guidance for sample size in dose-finding studies.

Main Methods:

  • * Examination of the sampling distribution of a nonparametric optimal design.
  • * Empirical derivation of an accuracy index for the CRM using linear regression.
  • * Development of analytical formulae for sample size approximation.

Main Results:

  • * Formulae provide sample size results similar to simulation for a phase I trial.
  • * The proposed formulae offer a quick and accurate approximation for CRM sample size.
  • * An R function 'getn' in the 'dfcrm' package is available for implementation.

Conclusions:

  • * The developed formulae are applicable to the Bayesian CRM.
  • * The derivation approach can be extended to other dose-finding methods.
  • * Simulation is recommended for validating results when using the formulae with other methods.