পদ্ধতির তুলনা করুন

	ঝুঁকি-সমন্বিত টিকে থাকার বিশ্লেষণ	ইনভার্স প্রোবাবিলিটি অফ ট্রিটমেন্ট ওয়েটিং (IPW / IPTW)
ক্ষেত্র	মহামারীবিদ্যা	কার্যকারণ অনুমান
পরিবার	Process / pipeline	Regression model
উদ্ভবের বছর	1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s	2000
প্রবর্তক	D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others	Robins, Hernán & Brumback
ধরন	Observational and experimental analytical method	Causal inference weighting estimator
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	5	5
সারসংক্ষেপ	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.