방법 비교

	위험 조정 횡단 역학 연구	로지스틱 회귀
분야	역학	연구 통계
계열	Process / pipeline	Process / pipeline
기원 연도	1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century	1958
창시자	Rooted in classical cross-sectional epidemiology (Doll, Hill, Lilienfeld); risk-adjustment formalization attributed to Lisa Iezzoni and colleagues in health outcomes research (1990s)	David Roxbee Cox
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	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
별칭	risk-adjusted cross-sectional survey, case-mix adjusted cross-sectional study, standardized cross-sectional analysis, adjusted prevalence study	logit model, binomial logistic regression, LR
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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.