See below for information on pricing. For additional information please visit: http://epicurehelp.risksciences.com. To request a demo version or to purchase Epicure, contact us.
RSI, acquired Epicure from Hirosoft International Corporation, and is marketing Epicure while actively working with Dale Preston and his colleagues to improve and enhance Epicure’s capabilities.
Brief History and Description of Epicure 

Epicure was originally developed for regression modeling of radiation effects on cancer rates in atomic bomb survivors. The development was motivated by the recognition that models focused on the excess relative risk (i.e. the RR1) were more suitable for describing dose response and effect modification than the loglinear Coxregression proportional hazards model and by the need for explicitly modeling excess rates (rate differences) as a function of dose and other, often timedependent, factors.
For almost 30 years, Epicure has provided a powerful set of tools for risk regression using a rich class of models that extends the commonly used loglinear relative risk (Cox regression) and relative odds (logistic regression) models to include excess relative risk / odds models and excess rate models. Epicure also includes a module for the straightforward specification and creation of highly stratified personyear (or more generally eventtime) tables including, as needed, stratification on multiple time scales and timedependent factors (such as lagged cumulative doses). Epicure is an interactive, commanddriven program with a simple and intuitive, b ut powerful scripting language. This new release features a graphical user interface that makes the program even easier to use. While Epicure is the defacto standard for modeling radiation health effects, the models and methods in Epicure have been used for a wide variety of medical, public health, epidemiological, economic, environmental, and reliability data. The methods are particularly useful for doseresponse modeling and investigating joint effects of and interactions between multiple risk factors. Epicure’s DATAB module is the most powerful and flexible tool available for creating high dimensional person year (eventtime) tables for use in Poissonregressionbased analyses of survival data including analysis of standardized mortality/incidence ratios. 

Epicure Risk Model Examples 

The following examples describe some of the models that can be used for modeling risks and rates in Epicure. Additional details are available in the online manuals (http://epicurehelp.risksciences.com).
Classical relative risk / relative odds models:baseline*RR baseline – nonparametric, stratified, fully parametric RR – typically loglinear ()
Excess relative risk / excess odds ratio:ERR excess relative risk
o Easily fit categorical, threshold, and spline doseresponse models
Joint effects for excess relative risk / excess odds ratio:additive excess relative risk multiplicative excess relative risk model
Risk difference / Excess rate / excess odds models†:


Epicure Risk Regression Modules 

GMBO / PECAN – Binomial data including conditional and unconditional logistic regression
PEANUTS –Partiallikelihood methods for censored survival data (including Cox regression)
AMFIT – Grouped survival / (Poisson (piecewise constant hazard) data


Personyear (rate) table creation (DATAB Module)  


Epicure Features 

Powerful tools for dealing with timedependent exposures and risksEasy to use standard models or to extend to more general risk models
Likelihoodbased inference
Interactive


What’s New 



Ordering Epicure 

A single user license is 1200 USD but licensed users of the previous edition of Epicure can upgrade for 750 USD.
Optional annual maintenance is 240 USD. Our discount program is outlined below:
Contact epicure@risksciences.com if you need more information about Epicure models and features. If you wish to purchase Epicure 2.0, please provide us with your billing details and indicate how many licenses you would like to purchase. 

Selected examples of analyses using EPICURE:
 Grant et al. Risk of death among children of atomic bomb survivors after 62 years of followup: a cohort study. Lancet Oncol. 2015 Oct;16(13):131623. http://www.ncbi.nlm.nih.gov/pubmed/26384241
 Gudzenko et al. Nonradiation risk factors for leukemia: A casecontrol study among chornobyl cleanup workers in Ukraine. Environ Res. 2015 Oct;142:726. http://www.ncbi.nlm.nih.gov/pubmed/26117815
 Lee et al. Occupational ionising radiation and risk of basal cell carcinoma in US radiologic technologists (19832005). Occup Environ Med. 2015 Dec;72(12):8629. http://www.ncbi.nlm.nih.gov/pubmed/26350677
 Sokolnikov M, Preston D, Gilbert E, Schonfeld S, Koshurnikova N. Radiation effects on mortality from solid cancers other than lung, liver, and bone cancer in the Mayak worker cohort: 19482008. PLoS One. 2015;10(2):e0117784. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342229/
 Zablotska et al. Leukemia, lymphoma and multiple myeloma mortality (19501999) and incidence (19691999) in the Eldorado uranium workers cohort. Environ Res. 2014 Apr;130:4350. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4002578/
 Furukawa et al. Radiation and smoking effects on lung cancer incidence among atomic bomb survivors. Radiat Res. 2010 Jul;174(1):7282. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857029/
 Lubin and Caporaso. Cigarette smoking and lung cancer: modeling total exposure and intensity. Cancer Epidemiol Biomarkers Prev. 2006 Mar;15(3):51723. http://cebp.aacrjournals.org/content/15/3/517.long