Linganisha mbinu

	Uchanganuzi wa Unusurika wenye Marekebisho ya Hatari	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Epidemiolojia	Uhitimisho wa Kisababishi
Familia	Process / pipeline	Regression model
Mwaka wa asili	1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s	2000
Mwanzilishi	D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others	Robins, Hernán & Brumback
Aina	Observational and experimental analytical method	Causal inference weighting estimator
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	5
Muhtasari	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.