Salīdzināt metodes

	Risk-Adjusted Survival Analysis	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Epidemioloģija	Cēloņsakarību secināšana
Saime	Process / pipeline	Regression model
Izcelsmes gads	1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s	2000
Autors	D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others	Robins, Hernán & Brumback
Tips	Observational and experimental analytical method	Causal inference weighting estimator
Pirmavots	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	5	5
Kopsavilkums	Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.