Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Epidemioloģisks šķērsgriezuma pētījums ar riska korekciju× | Logistiskā regresija× | |
|---|---|---|
| Nozare≠ | Epidemioloģija | Pētniecības statistika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century | 1958 |
| Autors≠ | 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 |
| Tips≠ | Observational epidemiological design with statistical adjustment | Method |
| Pirmavots≠ | 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. DOI ↗ |
| Citi nosaukumi≠ | risk-adjusted cross-sectional survey, case-mix adjusted cross-sectional study, standardized cross-sectional analysis, adjusted prevalence study | logit model, binomial logistic regression, LR |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. |
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