ReviewFrontiers in veterinary science2020
Sample Size Estimation in Veterinary Epidemiologic Research.
Review in Frontiers in veterinary science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Diversity and Distribution ofVeterinary sciences · 2026Article
- First detection of an H5N2 subtype of Influenza A virus detected in Charadrius collaris from the Brazilian Pantanal.Scientific reports · 2026Article
- Nationwide serological, molecular, and spatial assessment of Q fever in dromedary camels (Veterinary world · 2026Article
- Equine sentinels and one health: a comprehensive serological survey of Crimean-Congo hemorrhagic fever virus in southeastern and Central Europe.Frontiers in veterinary science · 2026Article
- Perioperative drug management in non-critical companion animals: a retrospective study at a Spanish veterinary teaching hospital (2018-2022).The veterinary quarterly · 2025Article
- Article
- Apparent prevalence, lesion distribution and risk factors of bovine tuberculosis in cattle slaughtered at the Shashemene and Arsi Negelle municipal abattoirs, Ethiopia.One health (Amsterdam, Netherlands) · 2025Article
- Dairy farmers have minimal knowledge of bovine tuberculosis: A cross-sectional study in Bhutan.PLoS neglected tropical diseases · 2025Article
- Bovine Leptospirosis: Serology, Isolation, and Risk Factors in Dairy Farms of La Laguna, Mexico.Life (Basel, Switzerland) · 2025Article
- Methodological issues of retrospective surveys for measuring mortality of highly clustered diseases: case study of the 2014-16 Ebola outbreak in Bo District, Sierra Leone.Global health action · 2024Article
- Leveraging artificial intelligence to identify high-risk patients for postoperative sore throat: An observational study.Biomolecules & biomedicine · 2024Observational
- Prior COVID-19 infection among newly diagnosed tuberculosis patients in a tertiary care center in Tehran: A case-control study.Immunity, inflammation and disease · 2024Article
- Variation in Pen-Level Prevalence of BRD Bacterial Pathogens and Antimicrobial Resistance Following Feedlot Arrival in Beef Calves.Antibiotics (Basel, Switzerland) · 2024Article
- Serological Surveillance and Risk Factor Analysis for Parrot Bornavirus in Taiwan.Transboundary and emerging diseases · 2024Article
- Prevalence of physiological and perceptual markers of low energy availability in male academy football players: a study protocol for a cross-sectional study.BMJ open sport & exercise medicine · 2024Article
- Article
- Assessing the Prevalence and Potential Risks ofFoods (Basel, Switzerland) · 2023Article
- Contamination Levels and Phenotypic and Genomic Characterization of Antimicrobial Resistance inTropical medicine and infectious disease · 2023Article
- In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room.Porcine health management · 2023Article
- Seroprevalence and Risk Factors for Bovine Coronavirus Infection among Dairy Cattle and Water Buffalo in Campania Region, Southern Italy.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the design of intervention and observational epidemiological studies sample size calculations are used to provide estimates of the minimum number of observations that need to be made to ensure that the stated objectives of a study are met. Justification of the number of subjects enrolled into a study and details of the assumptions and methodologies used to derive sample size estimates are now a mandatory component of grant application processes by funding agencies. Studies with insufficient numbers of study subjects run the risk of failing to identify differences among treatment or exposure groups when differences do, in fact, exist. Selection of a number of study subjects greater than that actually required results in a wastage of time and resources. In contrast to human epidemiological research, individual study subjects in a veterinary setting are almost always aggregated into hierarchical groups and, for this reason, sample size estimates calculated using formulae that assume data independence are not appropriate. This paper provides an overview of the reasons researchers might need to calculate an appropriate sample size in veterinary epidemiology and a summary of sample size calculation methods. Two approaches are presented for dealing with lack of data independence when calculating sample sizes: (1) inflation of crude sample size estimates using a design effect; and (2) simulation-based methods. The advantage of simulation methods is that appropriate sample sizes can be estimated for complex study designs for which formula-based methods are not available. A description of the methodological approach for simulation is described and a worked example provided.
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