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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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Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials.

Xiaolei Lin1, Jiaying Lyu2, Shijie Yuan2

  • 1School of Data Science, Fudan University, Shanghai, China.

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Summary
This summary is machine-generated.

BayeSize is a new Bayesian inference tool for determining appropriate sample sizes in phase I clinical trials. It ensures reliable sample size estimation while controlling for statistical errors, aiding trial planning.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacometrics

Background:

  • Phase I clinical trials are crucial for dose-finding.
  • Accurate sample size determination is essential for trial efficiency and validity.
  • Statistical uncertainty necessitates robust methods for sample size assessment.

Purpose of the Study:

  • To introduce BayeSize, a Bayesian inference tool for sample size assessment in phase I clinical trials.
  • To provide a reference for investigators to determine sample size and associated scientific properties.
  • To facilitate informed planning of phase I clinical trials.

Main Methods:

  • BayeSize utilizes Bayesian inference and the concept of effect size in dose finding.
  • It employs a decision framework with composite hypotheses and two types of priors (fitting and sampling).
  • Sample size calculations are performed under constraints of statistical power and type I error.

Main Results:

  • Simulation results demonstrate that BayeSize provides reliable sample size estimations.
  • The method effectively manages type I and type II error rates.
  • BayeSize ensures the identification of the maximum tolerated dose within a defined interval.

Conclusions:

  • BayeSize offers a valuable tool for phase I clinical trial planning.
  • It provides appropriate sample size estimations, enhancing trial design.
  • Practical applications are supported by provided look-up tables and an R Shiny app.