เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดลองสุ่มและมีกลุ่มควบคุม (Randomized Controlled Trial - RCT)× | การวิเคราะห์การรอดชีพ× | |
|---|---|---|
| สาขาวิชา≠ | การออกแบบการทดลอง | สถิติการวิจัย |
| ตระกูล≠ | Hypothesis test | Process / pipeline |
| ปีกำเนิด≠ | 1948 | 1958 |
| ผู้ริเริ่ม≠ | James Lind (early precursor, 1747); modern formulation: Austin Bradford Hill & Medical Research Council (1948) | Edward L. Kaplan and Paul Meier |
| ประเภท≠ | Interventional comparative study | Method |
| แหล่งต้นตำรับ≠ | Schulz, K.F., Altman, D.G., Moher, D., for the CONSORT Group (2010). CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials. BMJ, 340, c332. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| ชื่อเรียกอื่น≠ | RCT, randomised controlled trial, clinical trial, Randomize Kontrollü Çalışma (RCT) Tasarımı | Kaplan-Meier analysis, Cox regression, TTE analysis |
| ที่เกี่ยวข้อง≠ | 7 | 3 |
| สรุป≠ | A randomized controlled trial (RCT) is the gold standard experimental design in clinical and health research, in which participants are randomly allocated to a treatment group or a control group so that the effect of an intervention can be measured with the highest possible degree of internal validity. The modern parallel-group RCT was formalized by Austin Bradford Hill and the Medical Research Council in their landmark streptomycin trial of 1948, and its reporting is governed today by the CONSORT 2010 guidelines (Schulz et al., 2010). | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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