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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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Controlled nuclear fission reactions are used to generate electricity. Any nuclear reactor that produces power via the fission of uranium or plutonium by bombardment with neutrons has six components: nuclear fuel consisting of fissionable material, a nuclear moderator, a neutron source, control rods, reactor coolant, and a shield and containment system.
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Genotyping of Plant and Animal Samples without Prior DNA Purification
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Bayesian sample size re-estimation using power priors.

T B Brakenhoff1, Kcb Roes1, S Nikolakopoulos1

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.

Statistical Methods in Medical Research
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

This study enhances sample size calculations for randomized controlled trials by using Bayesian methods to incorporate prior variance information. It introduces power priors for better control over trial operational characteristics.

Keywords:
BayesianSample sizeborrowingmonitoringpower priorrandomized controlled trialre-estimationvariance

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Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Sample size determination in randomized controlled trials (RCTs) traditionally relies on frequentist methods.
  • Accurate variance estimation is crucial, often based on limited prior data, especially in small populations.
  • Bayesian approaches offer a framework to formally integrate prior information on variance with new data.

Purpose of the Study:

  • To adapt existing methodologies for sample size re-estimation in RCTs.
  • To propose the use of power priors for controlling operational characteristics in Bayesian trial design.
  • To improve the reliability of Bayesian sample size calculations.

Main Methods:

  • Utilizing Bayesian inference to model uncertainty in variance estimation through prior distributions.
  • Adapting previous methods to enable sample size re-estimation during a trial.
  • Implementing power priors to manage trial operating characteristics.

Main Results:

  • The proposed Bayesian approach allows for direct calculation of trial probabilities, such as the probability of a conclusive trial.
  • Sample size re-estimation methodology is adapted for practical application.
  • Power priors demonstrate potential for controlling frequentist operational characteristics.

Conclusions:

  • Bayesian methods provide a robust framework for sample size determination and re-estimation in RCTs.
  • The use of power priors can enhance the control over trial operating characteristics, even when prior information is limited.
  • Accurate incorporation of prior variance information is essential for trustworthy Bayesian sample size calculations.