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| 다기관 증례 시리즈× | 다기관 환자-대조군 연구× | |
|---|---|---|
| 분야 | 역학 | 역학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Mid-to-late 20th century (collaborative multi-site reporting common by 1970s–1980s) | Mid-20th century; multicenter framework formalised 1970s–1980s |
| 창시자≠ | Evolved from single-center case series practice; formalized in 20th century clinical reporting | Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980) |
| 유형≠ | Observational descriptive study | Observational analytical epidemiological design |
| 원전≠ | Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2012). COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology and prognosis. PLoS Medicine, 9(2), e1001175. link ↗ | Breslow, N. E., & Day, N. E. (1980). Statistical Methods in Cancer Research. Volume I: The Analysis of Case-Control Studies. IARC Scientific Publications No. 32. International Agency for Research on Cancer, Lyon. ISBN: 978-9283211327 |
| 별칭 | multi-site case series, multicentre case series, collaborative case series, multi-institutional case series | multisite case-control study, collaborative case-control study, pooled case-control study, multi-institutional case-control study |
| 관련≠ | 5 | 6 |
| 요약≠ | A multicenter case series is an observational descriptive study in which consecutive or selected patients sharing a defined clinical condition are enrolled and followed at two or more independent clinical sites. By pooling cases across institutions, researchers achieve larger sample sizes and greater demographic and clinical diversity than a single-center series permits, enabling more reliable description of disease presentation, management patterns, and outcomes for rare or uncommon conditions. | A multicenter case-control study is an observational design that identifies individuals who have developed a disease (cases) and disease-free comparators (controls) across two or more study sites simultaneously. By pooling recruitment across hospitals, clinics, or geographic regions, the design achieves larger sample sizes, captures exposure variability over broader populations, and improves the statistical power needed to detect modest odds ratios for rare or heterogeneous diseases. |
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