השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| ניתוח קפלן-מאייר – אמידת הישרדות לא-פרמטרית× | מודל הסיכונים היחסיים של קוקס× | |
|---|---|---|
| תחום | אפידמיולוגיה | אפידמיולוגיה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 1958 | 1972 |
| הוגה השיטה≠ | Edward L. Kaplan and Paul Meier | Sir David Roxbee Cox |
| סוג≠ | Nonparametric survival estimator | Semi-parametric regression model |
| מקור מכונן≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| כינויים | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve | Cox regression, Cox PH model, proportional hazards model, CPH |
| קשורות | 5 | 5 |
| תקציר≠ | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
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