방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 위험 조정 사례-교차 설계× | 위험 조정 코호트 연구× | |
|---|---|---|
| 분야 | 역학 | 역학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1991 (base design); risk-adjustment extensions from mid-1990s onward | Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s) |
| 창시자≠ | Malcolm Maclure (case-crossover base); extensions incorporating covariate risk adjustment developed in subsequent pharmacoepidemiology literature | Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century |
| 유형≠ | Observational analytic epidemiological design | Observational epidemiological study design with statistical confounding control |
| 원전≠ | Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| 별칭 | adjusted case-crossover study, covariate-adjusted case-crossover, risk-controlled case-crossover, case-crossover with risk adjustment | adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort |
| 관련 | 4 | 4 |
| 요약≠ | The risk-adjusted case-crossover design is a self-matched epidemiological method that compares a person's exposure during a brief hazard window immediately preceding an acute event to their exposure during one or more control windows from the same individual, while formally accounting for time-varying or time-fixed covariates that could confound the exposure-event relationship. By using each case as their own control, stable individual-level confounders are automatically cancelled, while covariate adjustment handles residual time-varying risks. | A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial. |
| ScholarGate데이터셋 ↗ |
|
|