Comparer des méthodes

	Étude épidémiologique pragmatique transversale	Échantillonnage en grappes
Domaine	Épidémiologie	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	Mid-20th century onwards; pragmatic framing prominent from 1967	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Classical epidemiology tradition; pragmatic framing refined by Schwartz & Lellouch (1967) and subsequent real-world evidence literature	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Observational epidemiological design	Probability sampling design
Source fondatrice	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alias	pragmatic cross-sectional survey, real-world cross-sectional study, observational cross-sectional study, prevalence survey	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	4	5
Résumé	A pragmatic cross-sectional epidemiological study measures the prevalence of exposures, outcomes, and risk factors in a defined population at a single point in time, conducted under real-world conditions rather than tightly controlled experimental settings. It provides a snapshot of the health status of a community or patient group, making it one of the most widely used designs for surveillance, needs assessment, and hypothesis generation in clinical and public-health epidemiology.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.