विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मिलान की गई कोहोर्ट स्टडी (Matched Cohort Study)× | प्रोपेंसिटी स्कोर मैचिंग× | |
|---|---|---|
| क्षेत्र≠ | महामारी विज्ञान | अनुसंधान सांख्यिकी |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | Mid-20th century; propensity-score variant 1983 | 1983 |
| प्रवर्तक≠ | Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983) | Paul Rosenbaum and Donald Rubin |
| प्रकार≠ | Observational analytic study design | Method |
| मौलिक स्रोत≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | 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≠ | matched follow-up study, paired cohort study, propensity-matched cohort, matched prospective study | PSM, propensity score weighting, covariate balance |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone. | 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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