Bandingkan metode

	Regresi Bahaya Proporsional yang Disesuaikan Risiko	Regresi Logistik
Bidang	Epidemiologi	Statistika Penelitian
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1972 (Cox model); risk adjustment widespread from 1980s	1958
Pencetus	D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward	David Roxbee Cox
Tipe	Multivariable survival regression	Method
Sumber perintis	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	adjusted Cox regression, multivariable Cox model, covariate-adjusted survival analysis, risk-adjusted survival model	logit model, binomial logistic regression, LR
Terkait	5	3
Ringkasan	Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the hazard of an event, producing hazard ratios (HRs) that are not distorted by baseline differences between comparison groups. It is the most widely used method for multivariable survival analysis in clinical and epidemiological research.	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.