विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| जोखिम-समायोजित उत्तरजीविता विश्लेषण× | प्रोपेंसिटी स्कोर मैचिंग× | |
|---|---|---|
| क्षेत्र≠ | महामारी विज्ञान | अनुसंधान सांख्यिकी |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s | 1983 |
| प्रवर्तक≠ | D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others | Paul Rosenbaum and Donald Rubin |
| प्रकार≠ | Observational and experimental analytical method | Method |
| मौलिक स्रोत≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. link ↗ | 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. DOI ↗ |
| उपनाम≠ | covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis | PSM, propensity score weighting, covariate balance |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | 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. | 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. |
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