Bayesian sample size determination for the accurate identification of the bacterial subtypes.
OBJECTIVE
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.


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 degrees 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 lall 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.


CONCLUSIONS
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.
Pourhoseingholi
Mohamad Amin
MA
Research Center of Gastroenterology and Liver diseases, Shahid Beheshti University, (M.C), Tehran, Iran. Aminphg@gmail.com
Dezfoulian
Anahita
A
Nasiri
Malihe
M
Dabiri
Hosein
H
Zali
Mohammad Reza
MR
D000818:Animals; D001499:Bayes Theorem; D004926:Escherichia coli / Q000145:classification / Q000235:genetics / Q000302:isolation & purification*; D015233:Models, Statistical; D010641:Phenotype; D010865:Pilot Projects; D016133:Polymerase Chain Reaction / Q000379:methods; D012107:Research Design; D018401:Sample Size; D015983:Selection Bias