เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การวิเคราะห์การรอดชีพแบบจับคู่× | แบบจำลองการถดถอยโคกซ์แบบสัดส่วนภาวะเสี่ยง× | |
|---|---|---|
| สาขาวิชา | ระบาดวิทยา | ระบาดวิทยา |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s | 1972 |
| ผู้ริเริ่ม≠ | Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983) | Sir David Roxbee Cox |
| ประเภท≠ | Observational study analytic method | Semi-parametric regression model |
| แหล่งต้นตำรับ≠ | Austin, P. C. (2014). Graphical assessments of the balance of propensity score matched samples: A SAS macro. Journal of Statistical Software, 58(7), 1-29. Also see Austin, P. C. (2017). A tutorial on multilevel survival analysis: Methods, models and applications. International Statistical Review, 85(2), 185-203. link ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| ชื่อเรียกอื่น | matched time-to-event analysis, propensity-matched survival analysis, matched Kaplan-Meier analysis, paired survival analysis | Cox regression, Cox PH model, proportional hazards model, CPH |
| ที่เกี่ยวข้อง≠ | 4 | 5 |
| สรุป≠ | Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estimating survival curves or hazard ratios, the approach reduces confounding bias in non-randomised studies and produces more credible comparisons of event-free survival between exposure groups. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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