เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การวิเคราะห์ Kaplan-Meier แบบหลายศูนย์× | การศึกษาแบบกลุ่มผู้ร่วมวิจัยหลายศูนย์ (Multicenter Cohort Study)× | |
|---|---|---|
| สาขาวิชา | ระบาดวิทยา | ระบาดวิทยา |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1958 (base method); multicenter designs common from 1970s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| ผู้ริเริ่ม≠ | Edward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onward | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| ประเภท≠ | Nonparametric survival analysis in a multicenter setting | Observational longitudinal study |
| แหล่งต้นตำรับ≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| ชื่อเรียกอื่น | pooled Kaplan-Meier, multi-site KM analysis, multicenter survival curve analysis, KM pooled analysis | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| ที่เกี่ยวข้อง≠ | 5 | 6 |
| สรุป≠ | Multicenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, enabling conclusions with greater statistical power and broader generalizability than single-center studies. | 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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