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Related Experiment Videos

Confidence limits for the population prevalence rate based on the negative binomial distribution

K J Lui1

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

Statistics in Medicine
|July 15, 1995
PubMed
Summary

This study extends confidence limit calculations for prevalence rates using inverse sampling. Increasing sample size, or number of cases, narrows confidence intervals for more reliable prevalence rate estimation.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Inference

Background:

  • Calculating confidence limits for prevalence rates is crucial in epidemiology.
  • Existing methods, like George and Elston's, may require adaptation for specific sampling strategies.
  • Inverse sampling offers an alternative approach to data collection.

Purpose of the Study:

  • To extend the George and Elston procedure for calculating confidence limits of the underlying prevalence rate.
  • To accommodate inverse sampling with a finite number of cases.
  • To quantitatively assess the impact of sample size on confidence interval width.

Main Methods:

  • Extension of the George and Elston procedure for inverse sampling.
  • Quantitative analysis of the relationship between sample size and expected confidence interval length.

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  • Development of a summary table for minimum required cases.

    • Main Results:

    • The extension of the George and Elston procedure to inverse sampling is straightforward.
    • Increasing the number of requested cases reduces the expected length of confidence intervals.
    • A table is provided to determine the minimum number of cases for desired confidence interval precision.

    Conclusions:

    • The proposed extension provides a practical method for calculating confidence limits in inverse sampling.
    • The findings offer guidance on sample size determination for accurate prevalence rate estimation.
    • The study clarifies the relationship between confidence limits in this context and related statistical measures.