Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Multivariate Cohort Research× | Overlevingsanalyse× | |
|---|---|---|
| Vakgebied≠ | Onderzoeksontwerp | Onderzoeksstatistiek |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward | 1958 |
| Grondlegger≠ | Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues | Edward L. Kaplan and Paul Meier |
| Type≠ | Observational quantitative research design | Method |
| Oorspronkelijke bron≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Aliassen≠ | multivariate cohort study, cohort study with multivariate analysis, multivariable cohort design, multivariate longitudinal cohort | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | Multivariate cohort research follows a defined group of individuals forward in time, collecting data on multiple exposures, outcomes, and covariates simultaneously. By applying multivariate statistical models — such as Cox regression, mixed-effects models, or structural equation models — researchers can disentangle the independent contributions of several predictors to one or more outcomes while controlling for confounders. The design is widely used in epidemiology, public health, psychology, and social sciences. | 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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