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	Poisson Rate Regression	Self-Controlled Case Series
Dziedzina	Social Epidemiology	Social Epidemiology
Rodzina	Regression model	Process / pipeline
Rok powstania	1983	1995
Twórca	E. L. Frome (rate formulation); A. C. Cameron & P. K. Trivedi (modern count-data treatment)	C. Paddy Farrington
Typ	Generalized linear model for event rates and counts with log link and person-time offset	Within-person case-only design for transient exposures and acute outcomes
Źródło pierwotne	Frome, E. L. (1983). The Analysis of Rates Using Poisson Regression Models. Biometrics, 39(3), 665-674.	Farrington, C. P. (1995). Relative Incidence Estimation from Case Series for Vaccine Safety Evaluation. Biometrics, 51(1), 228-235.
Inne nazwy	Poisson Regression for Rates, Log-Linear Rate Model, Incidence-Rate-Ratio Regression, Poisson Regression with Offset	SCCS, Case Series Method, Within-Person Comparison Design, Farrington Method
Pokrewne	3	3
Podsumowanie	Poisson rate regression is the standard generalized linear model for analyzing event rates and counts, such as the number of deaths, hospitalizations, or new cases observed over a span of person-time. It models the logarithm of the expected event rate as a linear function of covariates, using a Poisson likelihood and a log link, and accommodates differing amounts of exposure by including the log of person-time as an offset. Because coefficients enter on the log scale, their exponentials are incidence-rate ratios that quantify multiplicative effects on the rate. The rate formulation was crystallized in Frome's 1983 Biometrics paper, and the model sits within the broader count-data framework developed comprehensively by Cameron and Trivedi, who also detail its central practical concern: overdispersion, where the variance exceeds the Poisson assumption and standard errors must be corrected.	The self-controlled case series, or SCCS, is a case-only study design for estimating the association between a transient exposure and an acute event by comparing each individual's event rate during exposed time windows with their rate during unexposed time windows. Developed by Paddy Farrington in 1995 for vaccine safety evaluation, it uses data only on people who experienced the outcome, and because each person serves as their own control, it automatically eliminates all fixed within-person confounders — genetics, sex, chronic conditions, socioeconomic position — without ever measuring them. A conditional Poisson likelihood removes the individual-level baseline rate and yields a relative incidence comparing risk to control periods. Whitaker, Farrington, Spiessens and Musonda's 2006 Statistics in Medicine tutorial is the standard practical guide to fitting and interpreting the model.