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Calculating excess risks

Comp_Exrisk.Rd

Calculate the excess risk from a risk model under a specified exposure scenario.

Usage

Comp_Exrisk(exposure, riskmodel, option, per = 1)

Arguments

exposure

a list object that specifies the exposure scenario, which contains agex (a single value or a vector for age(s) at exposure), 'doseGy' (a single value or a vector of dose(s) in Gy), and 'sex' (1 or 2 for male or female).

riskmodel

a list object that specifies the risk model, which contains two list objects named err for excess relative rate model and 'ear' for excess absolute rate model, each of which contains a vector 'para' for model parameter estimates and a function 'f' to compute the excess risk given a parameter vector and exposure information (e.g., dose, age at exposure, sex, attained age).

option

a list object that specifies optional settings for risk calculation, which contains an integer value 'maxage' for the maximum age to follow up and a value 'err_wgt' for the weight for risk transfer (1=err, 0=ear).

per

an integer value for the risk denominator (default=1).

Value

information of calculated excess risk (data.frame)

See also

LSS_mortality, LSS_incidence

Examples

 # The following examples use default data provided in CanEpiRisk package
 # for riskmodels (LSS_mortality and LSS_incidence) derived from Life Span Study
 # and baseline mortality and incidence rates for WHO global regions (Mortality and Incidence).

 # Example 1: allsolid mortality, Region-1, female, 0.1Gy at age 15, followed up to age 100, LSS linear ERR
 exp1 <- list( agex=5, doseGy=0.1, sex=2 )   # exposure scenario
 ref1 <- list( baseline=Mortality[[1]]$allsolid,        # baseline rates
              mortality=Mortality[[1]]$allcause )       # all-cause mortality
 mod1 <- LSS_mortality$allsolid$L                       # risk model
 opt1 <- list( maxage=100, err_wgt=1, n_mcsamp=10000 )  # option
 CER(  exposure=exp1, reference=ref1, riskmodel=mod1, option=opt1 ) * 10000 # cases per 10,000
#>         mle        mean      median  ci_lo.2.5% ci_up.97.5% 
#>    221.1383    227.0030    221.3601    147.4071    337.7613