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Home / Publications / Sample size for estimating a binomial proportion: comparison of different methods.

Sample size for estimating a binomial proportion: comparison of different methods.

  • Authors: Gonçalves L, Pascoal C, Pires A, Rosario de Oliveira M
  • Journal: Journal of Applied Statistics
  • Link: https://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=7&SID=T1zdDDEYC1eBxhDSCne&page=1&doc=2

The poor performance of the Wald method for constructing confidence intervals (CIs) for a binomial proportion has been demonstrated in a vast literature. The related problem of sample size determination needs to be updated and comparative studies are essential to understanding the performance of alternative methods. In this paper, the sample size is obtained for the Clopper-Pearson, Bayesian (Uniform and Jeffreys priors), Wilson, Agresti-Coull, Anscombe, and Wald methods. Two two-step procedures are used: one based on the expected length (EL) of the CI and another one on its first-order approximation. In the first step, all possible solutions that satisfy the optimal criterion are obtained. In the second step, a single solution is proposed according to a new criterion (e.g. highest coverage probability (CP)). In practice, it is expected a sample size reduction, therefore, we explore the behavior of the methods admitting 30% and 50% of losses. For all the methods, the ELs are inflated, as expected, but the coverage probabilities remain close to the original target (with few exceptions). It is not easy to suggest a method that is optimal throughout the range (0, 1) for p. Depending on whether the goal is to achieve CP approximately or above the nominal level different recommendations are made.

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About GHTM

GHTM is a R&D Unit that brings together researchers with a track record in Tropical Medicine and International & Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

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