Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Проспективне вкладене дослідження випадок-контроль× | Аналіз виживаності× | |
|---|---|---|
| Галузь≠ | Епідеміологія | Статистика досліджень |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1977 | 1958 |
| Автор методу≠ | D.C. Thomas (formal description); building on Mantel (1973) and Liddell, McDonald & Thomas (1977) | Edward L. Kaplan and Paul Meier |
| Тип≠ | Observational analytic design | Method |
| Основоположне джерело≠ | Thomas, D.C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. By F.D.K. Liddell, J.C. McDonald, and D.C. Thomas. Journal of the Royal Statistical Society, Series A, 140(4), 469-491. link ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Інші назви≠ | prospective NCC, nested case-control within prospective cohort, prospective case-control within cohort, incident NCC | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | A prospective nested case-control study enrolls a cohort before disease onset, follows participants forward in time, and then — once cases develop — samples matched controls from those still at risk at the time each case occurs. By embedding the case-control comparison inside a prospective cohort, the design combines the causal clarity of longitudinal follow-up with the cost efficiency of analysing only a fraction of the cohort's stored specimens or records. | 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. |
| ScholarGateНабір даних ↗ |
|
|