Salīdzināt metodes

	Saskaņotā Kaplan-Meier analīze	Propensity Score Matching
Nozare	Epidemioloģija	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1958 (KM); matched application formalized 1980s–2000s	1983
Autors	Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983)	Paul Rosenbaum and Donald Rubin
Tips	Nonparametric survival analysis with observational confounder control	Method
Pirmavots	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Citi nosaukumi	KM analysis in matched cohorts, propensity-matched survival curves, matched survival analysis, paired Kaplan-Meier	PSM, propensity score weighting, covariate balance
Saistītās	6	3
Kopsavilkums	Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling for confounders that would otherwise distort treatment or exposure comparisons in observational data.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.