So sánh phương pháp

	Phân tích chuỗi ca bệnh đã điều chỉnh rủi ro	Nghiên cứu đoàn hệ điều chỉnh rủi ro
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s	Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)
Người khởi xướng	Copeland, Jones & Walters (POSSUM score, 1991); broader risk-adjustment methodology developed across surgical and critical care audit literature	Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century
Loại	Observational study design with statistical risk correction	Observational epidemiological study design with statistical confounding control
Công trình gốc	Copeland, G. P., Jones, D., & Walters, M. (1991). POSSUM: a scoring system for surgical audit. British Journal of Surgery, 78(3), 355–360.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Tên gọi khác	risk-stratified case series, adjusted case series, risk-corrected case series	adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort
Liên quan	5	4
Tóm tắt	A risk-adjusted case series is an observational study design that reports outcomes for a consecutive or defined group of patients undergoing the same procedure or sharing a condition, while statistically correcting for differences in patient-level baseline risk. Rather than presenting raw complication or mortality rates, it compares observed outcomes against expected rates derived from a validated scoring model (e.g., POSSUM, APACHE, ASA grade), enabling fairer evaluation of clinical performance across institutions or over time.	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.