01. What is Epicure?
Quote from Eric Grant on 09/12/2023, 11:57 amEpicure is a program for estimation of and inference about how rates and risks (probabilities) depend on factors such as age, sex, and dose. It is especially useful for describing dose-response effects and the joint effects of different potential risk factors in cohort and case-control studies.
Epicure can be easily fit the log-linear models that are commonly used for Cox regression and both conditional and unconditional logistic regression. In addition, it is easy to specify more general models such as: linear dose response models with dose effect modification or models with additive, multiplicative or more complex joint effects for different risk factors (e.g. the joint effects of air pollution and smoking on cancer rates). The Poisson regression methods available in epicure make it possible (and easy) to describe and examine how baseline rates depend on factors such as age and sex and the nature of the relative or excess rates for risk factors of interest.
Epicure includes a unique and powerful module (DATAB) that can be used to create cross-classifications of time-at-risk (typically person-years) and cases on multiple factors, including multiple time-scales (such as attained age, duration of exposure, or calendar time ) and other time-dependent factor (such as cumulative exposure, number of live births, smoking status, years smoked, and amount smoked).
Epicure is a program for estimation of and inference about how rates and risks (probabilities) depend on factors such as age, sex, and dose. It is especially useful for describing dose-response effects and the joint effects of different potential risk factors in cohort and case-control studies.
Epicure can be easily fit the log-linear models that are commonly used for Cox regression and both conditional and unconditional logistic regression. In addition, it is easy to specify more general models such as: linear dose response models with dose effect modification or models with additive, multiplicative or more complex joint effects for different risk factors (e.g. the joint effects of air pollution and smoking on cancer rates). The Poisson regression methods available in epicure make it possible (and easy) to describe and examine how baseline rates depend on factors such as age and sex and the nature of the relative or excess rates for risk factors of interest.
Epicure includes a unique and powerful module (DATAB) that can be used to create cross-classifications of time-at-risk (typically person-years) and cases on multiple factors, including multiple time-scales (such as attained age, duration of exposure, or calendar time ) and other time-dependent factor (such as cumulative exposure, number of live births, smoking status, years smoked, and amount smoked).