Salīdzināt metodes

	Propensity Score Matching	Izdzīvošanas analīze
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1983	1958
Autors	Paul Rosenbaum and Donald Rubin	Edward L. Kaplan and Paul Meier
Tips	Method	Method
Pirmavots	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.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Citi nosaukumi	PSM, propensity score weighting, covariate balance	Kaplan-Meier analysis, Cox regression, TTE analysis
Saistītās	3	3
Kopsavilkums	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.	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.