Bandingkan kaedah

	Penilaian Ujian Saringan Terlaras Risiko	Regresi Logistik
Bidang	Epidemiologi	Statistik Penyelidikan
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	Late 1990s–2000s (formal statistical framework ~1997–2009)	1958
Pengasas	Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology)	David Roxbee Cox
Jenis	Analytical study design	Method
Sumber perintis	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198565826	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE	logit model, binomial logistic regression, LR
Berkaitan	6	3
Ringkasan	Risk-adjusted screening test evaluation assesses the sensitivity, specificity, and overall discriminatory accuracy of a screening test after accounting for patient-level risk factors (covariates) that independently influence test results or disease prevalence. By conditioning performance metrics on observed covariates — age, sex, comorbidities, or prior screening history — this approach yields accuracy estimates that are not confounded by differences in population risk profiles, enabling fair comparisons across subgroups or study settings.	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.