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| 위험 조정 횡단 역학 연구× | 성향 점수 매칭× | |
|---|---|---|
| 분야≠ | 역학 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century | 1983 |
| 창시자≠ | Rooted in classical cross-sectional epidemiology (Doll, Hill, Lilienfeld); risk-adjustment formalization attributed to Lisa Iezzoni and colleagues in health outcomes research (1990s) | Paul Rosenbaum and Donald Rubin |
| 유형≠ | Observational epidemiological design with statistical adjustment | Method |
| 원전≠ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195083385 | 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 ↗ |
| 별칭≠ | risk-adjusted cross-sectional survey, case-mix adjusted cross-sectional study, standardized cross-sectional analysis, adjusted prevalence study | PSM, propensity score weighting, covariate balance |
| 관련≠ | 4 | 3 |
| 요약≠ | A risk-adjusted cross-sectional epidemiological study measures the prevalence of health outcomes or exposures in a defined population at a single point in time, then applies statistical risk-adjustment methods — such as regression standardization, direct or indirect standardization, or propensity scoring — to remove the distorting influence of differences in patient case-mix across comparison groups. The approach is widely used in health services research, comparative effectiveness, and clinical quality assessment. | 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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