Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Согласованное вложенное случай-контроль× | Метод подбора на основе оценки склонности× | |
|---|---|---|
| Область≠ | Эпидемиология | Статистика исследований |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1970s | 1983 |
| Автор метода≠ | Mantel (1973), Thomas (1977); formalized by Breslow & Day (1980) | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Observational analytic study design | Method |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | matched NCC study, nested case-control with matching, matched risk-set sampling, incidence density matched case-control | PSM, propensity score weighting, covariate balance |
| Связанные≠ | 5 | 3 |
| Сводка≠ | A matched nested case-control study is an efficient observational design embedded within a defined cohort. When a participant develops the outcome of interest (a case), a small number of controls are sampled from those still at risk at that moment and matched to the case on key variables such as age, sex, or calendar time. This design preserves the temporal structure of the underlying cohort while sharply reducing the cost of exposure measurement. | 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 данных ↗ |
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