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Sample Size Calculation01:19

Sample Size Calculation

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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Assessment of small health risks based on exact sample sizes

K Abt1, A Gülich

  • 1University of Frankfurt/Main, Germany, Medical School, Department of Biomathematics, Germany.

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|January 30, 1996
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Summary

This study provides exact sample sizes for detecting increased event probabilities, crucial for clinical trial design and risk assessment. The findings aid in planning studies and evaluating empirical data effectively.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Accurate sample size calculation is essential for the statistical validity of clinical trials.
  • Assessing the probability of rare events requires precise methodologies to avoid erroneous conclusions.

Purpose of the Study:

  • To present exact sample sizes and critical numbers for rejecting a known low event probability in favor of a higher one.
  • To validate these calculations using confidence interval characteristics for unknown true event probabilities.

Main Methods:

  • Statistical analysis involving the calculation of exact sample sizes for hypothesis testing.
  • Validation through the characteristics of confidence intervals for event probabilities.
  • Demonstration of equivalence between relative risk tolerance and confidence interval upper limits.

Main Results:

  • Exact sample sizes are provided for event probabilities ranging from 10^-2 to 10^-6, with increases of 1.5- to 50-fold.
  • Calculations are validated for Type I (alpha) and Type II (beta) error levels of 0.05/0.10 and 0.10/0.05.
  • Equivalence is established between the maximum tolerated relative risk and the upper confidence limit of the true event probability.

Conclusions:

  • The presented tables offer a robust tool for determining necessary sample sizes in studies involving rare events.
  • The findings facilitate planned risk reduction strategies and the evaluation of existing data sets.
  • This work enhances the rigor of statistical inference in epidemiological and clinical research.