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Bayesian Sample Size Determination For The Accurate Identification Of The Bacterial Subtypes
Abstract
Background & Aim: Sample size estimation is a major component of the design of virtually every experiment in biosciences. Microbiologists face a challenge when allocating resources to surveys designed to determine the sampling unit of bacterial strains of interest. In this study we derived a Bayesian approach with a dirichlet prior based on a pilot study in to find an adequate sample size for E-coli subtypes’ determination.
Materials & Methods: Five strains of E.coli (st genes, eae gene, stx1 and stx2 genes, ial gene) were used in this study. The strains were grown
overnight at 37◦C to obtain strains in a linear growth phase, according to McFarland’s score. The resulting supernatants were used as templates in the PCR reactions. Then the results used with Dirichlet Distribution as a prior to find the Bayesian optimum sample size.
Results: 100 colonies were harvested from plate and examined in the PCR reaction, 50 colonies showed no specific genes, 40 detected as eae
gene (78.8%), 6 colonies stx2 gene (11.7%), 2 colonies st gene (3.9%), 2 colonies stxl2 gene (3.9%) and only 1 colony detected as lal1 gene
(1.9%). First according to the frequentist view sample size calculated indicating a different range of sample size from 25 colonies to unusual
number, 4475 colonies. Then using Bayesian approach by posterior expectations instead of pilot results sample size were fund from the range of 291 colonies to 443 colonies.
Conclusion: The results indicated that Bayesian approach technique leads to optimal sample size with similar power in compare to traditional
technique where sample size calculated without any prior information.
Materials & Methods: Five strains of E.coli (st genes, eae gene, stx1 and stx2 genes, ial gene) were used in this study. The strains were grown
overnight at 37◦C to obtain strains in a linear growth phase, according to McFarland’s score. The resulting supernatants were used as templates in the PCR reactions. Then the results used with Dirichlet Distribution as a prior to find the Bayesian optimum sample size.
Results: 100 colonies were harvested from plate and examined in the PCR reaction, 50 colonies showed no specific genes, 40 detected as eae
gene (78.8%), 6 colonies stx2 gene (11.7%), 2 colonies st gene (3.9%), 2 colonies stxl2 gene (3.9%) and only 1 colony detected as lal1 gene
(1.9%). First according to the frequentist view sample size calculated indicating a different range of sample size from 25 colonies to unusual
number, 4475 colonies. Then using Bayesian approach by posterior expectations instead of pilot results sample size were fund from the range of 291 colonies to 443 colonies.
Conclusion: The results indicated that Bayesian approach technique leads to optimal sample size with similar power in compare to traditional
technique where sample size calculated without any prior information.
