STROBE-Vet (STROBE Extension for Veterinary Epidemiology)

What it is

— STROBE-Vet (Strengthening the Reporting of Observational Studies in Epidemiology — Veterinary) is a reporting-guideline extension of the original STROBE Statement, tailored to observational studies (cohort, case-control, and cross-sectional) conducted in veterinary medicine and animal-health research. It does not replace STROBE: it inherits STROBE's 22 generic items and adds or modifies a subset to capture features that recur in veterinary epidemiology — multilevel/clustered data (animals nested within pens, herds, flocks, farms, or clinics), the choice and reporting of the unit of analysis (individual animal vs. herd/group), housing and management exposures, production-system context, animal-welfare and ethics reporting, and explicit description of the source population and sampling frame (often a convenience or production sample rather than a defined catchment). The Statement (Sargeant, O'Connor, Dohoo et al., 2016) was co-published across four journals — Journal of Veterinary Internal Medicine, Preventive Veterinary Medicine, Zoonoses and Public Health, and Journal of Food Protection — and is accompanied by an item-by-item Explanation and Elaboration document (O'Connor et al., 2016). It is hosted and indexed as a STROBE extension within the EQUATOR Network.

When to use

— Apply STROBE-Vet when reporting a completed observational study in animals or animal populations: prevalence/cross-sectional surveys of herds or flocks, cohort studies of production or companion animals, and case-control studies of veterinary outcomes, including studies drawing on veterinary administrative/production databases, herd registries, abattoir/surveillance data, or companion-animal clinical records (the veterinary analogue of human claims/EHR/registry data). Decision rule for choosing the right STROBE member: use plain STROBE for human observational studies; use RECORD / RECORD-PE for human studies built on routinely-collected health/pharmacoepidemiologic data; use STROBE-Vet specifically when the observational study is veterinary and you want the field-specific items (clustering, unit of analysis, housing/management, production context) reported. For controlled animal experiments (laboratory/pre-clinical in-vivo work) the relevant guideline is ARRIVE, not STROBE-Vet, because those are designed experiments, not observational studies.

What it requires

— STROBE-Vet enforces the STROBE reporting backbone — a structured title/abstract, background/objectives, an explicit study design named in the methods, setting and eligibility, clearly defined variables (exposures, outcomes, confounders, effect modifiers) with measurement/data sources, efforts to address bias, study size and quantitative methods, a participant/animal flow and descriptive table, main results with confounder-adjusted estimates and precision, and an honest limitations and generalizability discussion — and layers veterinary specifics on top: (1) the unit of analysis and clustering structure must be stated and carried through the analysis (animal vs. pen vs. herd; how within-cluster correlation was handled); (2) the source population, sampling frame, and selection process must be described, because veterinary samples are frequently non-random; (3) housing, management, and production-system covariates that drive exposure and outcome must be reported; (4) case/outcome and exposure definitions (including diagnostic-test characteristics where outcomes are test-defined — the veterinary parallel of phenotype/algorithm validation) must be explicit; and (5) ethics, welfare, and animal-use approvals must be reported. Framed against the same data-fitness and transparency problems that human RWE faces, STROBE-Vet pushes the author to make design, data provenance, time alignment, attrition, confounding control, and outcome-definition validity legible to the reader.

When NOT to use — limitations and common misapplications

— STROBE-Vet is a reporting checklist, not a methodological fix. Concrete failure modes: (1) Wrong domain — using STROBE-Vet for a human observational study; report human studies with STROBE, or RECORD/RECORD-PE when they use routinely-collected data, or follow HARPER/ENCePP for pharmacoepidemiologic protocols. (2) Wrong design class — using STROBE-Vet for a controlled animal experiment; designed in-vivo experiments are reported under ARRIVE. (3) Mistaking it for a risk-of-bias or quality-appraisal instrument — STROBE-Vet does not score study quality or grade bias; critical appraisal of non-randomized studies uses tools such as ROBINS-I, and it is a category error to "STROBE-score" a paper. (4) Checklist-as-theater — ticking items while leaving the unit of analysis, clustering, sampling frame, or outcome definition vague defeats the purpose; the value is the substantive transparency, not the completed grid. (5) Reporting completeness ≠ internal validity — a fully STROBE-Vet-compliant cross-sectional herd study with confounding by management practice is still confounded; the checklist makes the design legible, it does not make the estimate causal.

How it maps to this catalog

— This catalog's implementing concepts are written for human real-world evidence (claims/EHR/registry), so they are not literal implementations of a veterinary guideline. They are, however, direct structural analogues: the design and data problems STROBE-Vet asks you to report are the same problems these concepts operationalize, and a veterinary RWE study can borrow the same machinery against animal-health administrative, production, and clinical-record databases. Read each as the human-side worked example of a requirement STROBE-Vet imposes: - Design transparency and a trial-emulation frame for observational comparisons → target-trial-emulation and active-comparator-new-user. - Time alignment / index-date definition (the "when does follow-up start" item) → time-zero-index-date-alignment-rwe. - Outcome and exposure definition validity (test- or algorithm-defined cases) → diagnosis-phenotype-algorithm-1ip-2op-time-window-rwe and algorithm-validation. - Confounding control, including the management/housing covariates STROBE-Vet flags → high-dimensional-propensity-score-hdps-rwe and propensity-score-methods-psm-iptw. - Estimands and how intercurrent events are handled → estimands-ate-att-intercurrent-events-rwe. - Attrition / loss to follow-up and the participant-flow item → attrition-and-loss-to-follow-up-rwe. - Generalizability of the (often non-random) sample → generalizability-transportability-external-validity-rwe. - The administrative-database mechanics themselves → claims-analysis as the methodological template. Treat these as parallel structure, not as plug-ins: the clustering, animal-vs-herd unit of analysis, and production-system context are veterinary-specific and have no direct slug here.

Applied note (veterinary administrative / herd-registry / clinical-record RWE)

A retrospective cohort built from a swine-production database or a companion-animal practice-management EHR faces the same fitness- for-use questions as a human claims study: is enrollment/observation continuous, is the outcome definition validated, is time zero aligned to a real decision point, and is loss to follow-up informative? Report the unit of analysis (animal vs. litter vs. herd) and the within-cluster correlation handling explicitly, state the sampling frame and why it may not represent the target population, and pre-specify how diagnostic-test- defined outcomes were validated — exactly the transparency the human-RWE concept entries above demonstrate.