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| Съгласувано клинично изпитване Фаза III× | Съгласуване по показател на склонност× | |
|---|---|---|
| Област≠ | Епидемиология | Статистика за изследвания |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | Mid-20th century (matching in RCTs formalized ~1950s–1970s) | 1983 |
| Създател≠ | Fisher, R. A. (matching principles); adapted into confirmatory trial design over mid-20th century | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Controlled confirmatory clinical trial with matching | Method |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | matched controlled Phase III trial, Phase III matched-pair trial, matched confirmatory trial, matched late-phase RCT | PSM, propensity score weighting, covariate balance |
| Свързани≠ | 5 | 3 |
| Резюме≠ | A matched Phase III clinical trial is a confirmatory, late-stage controlled study in which each participant assigned to the experimental treatment is paired with one or more controls who share key prognostic characteristics — such as age, disease stage, or comorbidities — before treatment allocation. By ensuring baseline comparability at the level of matched pairs, the design reduces confounding and improves statistical efficiency in settings where simple randomization alone may produce imbalanced groups or where full randomization is logistically or ethically constrained. | 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. |
| ScholarGateНабор от данни ↗ |
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