手法を比較

	マルチセンター症例シリーズ	多施設共同症例対照研究
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	Mid-to-late 20th century (collaborative multi-site reporting common by 1970s–1980s)	Mid-20th century; multicenter framework formalised 1970s–1980s
提唱者	Evolved from single-center case series practice; formalized in 20th century clinical reporting	Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980)
種類	Observational descriptive study	Observational analytical epidemiological design
原典	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.	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
別名	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
関連	5	6
概要	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.