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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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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.
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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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Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Study Design in Statistics

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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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Updated: Sep 11, 2025

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Drift Parameter Based Sample Size Determination in Multi-Stage Bayesian Randomized Clinical Trials.

Yueyang Han1, Haolun Shi1, Jiguo Cao1

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.

Pharmaceutical Statistics
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

Calculating sample sizes for Bayesian randomized phase II trials is now faster. Our new method links group sequential and Bayesian designs, significantly reducing computational time for accurate sample size determination.

Keywords:
Bayesian designmulti‐stage designphase II clinical trialposterior probabilityrandomized trialsample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Statistics

Background:

  • Sample size determination in Bayesian randomized phase II trials is computationally intensive.
  • Existing methods pose challenges for feasibility and efficiency.

Purpose of the Study:

  • To develop a novel, computationally efficient approach for sample size calculations in Bayesian randomized phase II trials.
  • To reduce the computing time associated with sample size determination.

Main Methods:

  • Connecting group sequential design with Bayesian trial design.
  • Leveraging the proportional relationship between sample size and the squared drift parameter.
  • Employing regression analysis for accurate sample size pinpointing.

Main Results:

  • Significantly reduced computational burden for sample size calculations.
  • Validated approach through theoretical justification and extensive numerical evaluations.
  • Demonstrated efficiency across various trial scenarios (binary, normal, ordinal, survival endpoints).

Conclusions:

  • The proposed method offers a faster and more efficient algorithm for sample size determination in Bayesian phase II trials.
  • The R package "BayesSize" facilitates the practical application of these novel methods.
  • This approach enhances the feasibility of Bayesian trial designs in clinical research.