手法を比較

	Indirect Age Standardization	Poisson Rate Regression
分野	Social Epidemiology	Social Epidemiology
系統	Process / pipeline	Regression model
提唱年	2001	1983
提唱者	Classical demography / vital statistics (formalized in Preston, Heuveline & Guillot)	E. L. Frome (rate formulation); A. C. Cameron & P. K. Trivedi (modern count-data treatment)
種類	Rate-standardization pipeline for comparing populations under unstable stratum rates	Generalized linear model for event rates and counts with log link and person-time offset
原典	Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell Publishers. ISBN: 9781557864512	Frome, E. L. (1983). The Analysis of Rates Using Poisson Regression Models. Biometrics, 39(3), 665-674.
別名	Indirect Standardization, Standardized Mortality Ratio (SMR), Indirectly Standardized Rate, SMR Method	Poisson Regression for Rates, Log-Linear Rate Model, Incidence-Rate-Ratio Regression, Poisson Regression with Offset
関連	3	3
概要	Indirect age standardization is a demographic technique for comparing the overall event rate (most often mortality) of a study population to a reference, when the population's own age-specific rates are too sparse or unstable to standardize directly. Instead of applying the study population's rates to a standard age structure, it does the reverse: it applies a stable set of standard age-specific rates to the study population's age distribution to compute the number of events that would be expected under the standard schedule. The ratio of observed to expected events is the standardized mortality (or morbidity) ratio, the SMR, and multiplying it by the standard's crude rate yields an indirectly standardized rate. The method is a staple of vital statistics and occupational and small-area epidemiology, and is developed from first principles in Preston, Heuveline and Guillot's demography text.	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.