De los datos preliminares al diseño robusto: estimación de muestras para estudios de Anaplasma phagocytophilum y otros estudios de prevalencia

Anayansi Araúz; Ángel Santana Bolívar; Joel Alexis Morales Allard; Abraham De Sedas Muñoz; Yostin Jesús Añino Ramos; Karla Jeanneette Agrazal; Emilio Romero

doi:10.30972/vet.3719106

Authors

Anayansi Araúz Facultad de Medicina Veterinaria, Universidad de Panamá, Ciudad de Panamá, Panamá. https://orcid.org/0009-0000-8017-0752
Ángel Santana Bolívar Facultad de Medicina Veterinaria, Universidad de Panamá, Ciudad de Panamá, Panamá https://orcid.org/0009-0004-8386-2681
Joel Alexis Morales Allard Facultad de Medicina Veterinaria, Universidad de Panamá, Ciudad de Panamá, Panamá https://orcid.org/0009-0001-7630-4700
Abraham De Sedas Muñoz Departamento de Estadística, Facultad de Ciencias Naturales Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá. https://orcid.org/0009-0006-7170-8649
Yostin Jesús Añino Ramos Museo de Invertebrados G.B. Fairchild, Universidad de Panamá, Ciudad de Panamá, Panamá. Estación Científica Coiba (COIBA AIP), Clayton, Panamá. https://orcid.org/0000-0002-8870-8155
Karla Jeanneette Agrazal Museo de Invertebrados G.B. Fairchild, Universidad de Panamá, Ciudad de Panamá, Panamá. https://orcid.org/0000-0002-2974-9344
Emilio Romero Departamento de Fisiología y Comportamiento Animal, Facultad de Ciencias Naturales Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá. https://orcid.org/0000-0002-3262-0656

DOI:

https://doi.org/10.30972/vet.3719106

Keywords:

Prevalence studies, Precision, Sample size, ScalaR

Abstract

Accurate sample size estimation is essential in prevalence studies to ensure statistically valid conclusions and efficient resource allocation. This technical note presents an approach for estimating sample size in prevalence studies of Anaplasma phagocytophilum in equines. A preliminary PCR-based survey in Panama detected one positive case among 36 animals (2.78%). Based on these results, expected prevalences of 2%, 3%, 8%, and 9% were used to model future sample sizes using the ScalaR function in R, incorporating absolute precisions (± 0.75%, ± 1.5%, ± 2%) and a 10% adjustment for potential data loss. The estimated sample sizes ranged from 233 (2% prevalence, ± 1.9%) to 6,216 animals (9% prevalence, ± 0.75%). These findings illustrate how minor variations in assumed prevalence or desired precision can substantially influence the sample size required for reliable epidemiological surveillance.

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