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| Multicenter Case-Crossover Design× | Multicentrikus kohorszvizsgálat× | |
|---|---|---|
| Tudományterület | Epidemiológia | Epidemiológia |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1991 (core design); multicenter extensions 1990s–2000s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Megalkotó≠ | Malcolm Maclure (single-center design, 1991); multicenter applications developed through 1990s–2000s environmental and pharmacoepidemiology literature | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| Típus≠ | Observational epidemiological design | Observational longitudinal study |
| Alapmű≠ | Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Alternatív nevek | multi-site case-crossover study, multicenter self-matched crossover, multi-center transient exposure study, MCCO study | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| Kapcsolódó≠ | 4 | 6 |
| Összefoglaló≠ | The multicenter case-crossover design is an observational epidemiological method that investigates whether brief, transient exposures trigger acute health events by comparing each case's exposure just before the event to their own exposure during matched control periods — with data collected from two or more independent clinical or geographic sites to increase power, external validity, and the ability to detect site-level effect modification. | A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research. |
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