Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Studio epidemiologico pragmatico trasversale× | Campionamento a grappoli× | |
|---|---|---|
| Campo≠ | Epidemiologia | Metodologia delle indagini |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | Mid-20th century onwards; pragmatic framing prominent from 1967 | Early-to-mid 20th century; canonical treatment 1953/1977 |
| Ideatore≠ | 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 |
| Tipo≠ | Observational epidemiological design | Probability sampling design |
| Fonte seminale≠ | 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 |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. |
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