Confidence limits for the population prevalence rate based on the negative binomial distribution.
This paper shows that the extension of the simple procedure of George and Elston in calculation of confidence limits for the underlying prevalence rate to accommodate any finite number of cases in inverse sampling is straightforward. To appreciate the fact that the length of the confidence interval calculated on the basis of the first single case may be too wide for general utility, I include a quantiative discussion on the effect due to an increase in the number of cases requested in the sample on the expected length of confidence intervals. To facilitate further the application of the results presented in this paper, I present a table that summarizes in a variety of situations the minimum required number of cases for the ratio of the expected length of a confidence interval relative to the underlying prevalence rate to be less than or equal to a given value. I also include a discussion on the relation between Clemans's confidence limits on the expected number of trials before the failure of a given device and those presented here.
D016010:Binomial Distribution; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D015995:Prevalence; D018401:Sample Size