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Sample size for the exact conditional test under inverse sampling

K J Lui1

  • 1Department of Mathematical Sciences, College of Sciences, San Diego State University, CA 92182-0314, USA.

Statistics in Medicine
|March 30, 1996
PubMed
Summary
This summary is machine-generated.

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This study introduces inverse sampling, a method for determining the minimum number of subjects needed for studies. It provides a table to help researchers calculate sample sizes for desired statistical power.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research Design

Background:

  • Inverse sampling is a statistical technique where data collection continues until a specific number of 'index' subjects are identified.
  • Accurate sample size calculation is crucial for study power and validity.

Purpose of the Study:

  • To derive a procedure for calculating the minimum required number of index subjects using exact conditional tests under inverse sampling.
  • To quantitatively assess the impact of the number of index subjects on statistical power calculations.
  • To provide practical tools, including a summary table, to aid researchers in employing inverse sampling.

Main Methods:

  • Derivation of sample size calculation procedures based on exact conditional tests for inverse sampling.
  • Quantitative analysis of the effect of index subject numbers on power.

Related Experiment Videos

  • Development of a summary table for minimum required index subjects at 0.90 and 0.80 power levels (0.05 significance level).
  • Discussion of approximation sample size formulas using variance-stabilizing transformations and large sample theory.

    • Main Results:

    • A procedure for calculating minimum index subjects under inverse sampling is established.
    • The study quantifies how the number of index subjects influences statistical power.
    • A table is provided, offering specific minimum subject counts for common power levels (0.90, 0.80) at a 0.05 significance level.

    Conclusions:

    • The derived methods and provided tables facilitate the effective use of inverse sampling in study designs.
    • Researchers can more accurately determine necessary sample sizes, enhancing study efficiency and power.
    • The findings support informed decision-making in research planning involving inverse sampling strategies.