So sánh phương pháp

	Phân tích sống còn ghép cặp	Kiểm định Log-Rank để so sánh các đường cong sống sót
Lĩnh vực	Dịch tễ học	Phân tích sống còn
Họ	Process / pipeline	Survival analysis
Năm ra đời	1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s	1966
Người khởi xướng	Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983)	Mantel, N.
Loại	Observational study analytic method	Non-parametric hypothesis test
Công trình gốc	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.	Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170.
Tên gọi khác	matched time-to-event analysis, propensity-matched survival analysis, matched Kaplan-Meier analysis, paired survival analysis	Mantel log-rank test, Mantel-Cox test, log-rank sağkalım testi, Log-Rank Testi
Liên quan	4	2
Tóm tắt	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 log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful.