Epi: DMepi – R documentation

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Documentation

Epi

DMepi

Epidemiological rates for diabetes in Denmark 1996–2015

Description

Usage

data("DMepi")

Format

A data frame with 4200 observations on the following 8 variables.

sex: a factor with levels M, F
A: Age class, 0–99
P: Calendar year, 1996–2016
X: Number of new diagnoses of diabetes among persons without diabetes
D.nD: Number of deaths among persons without diabetes
Y.nD: Person-years among persons without diabetes
D.DM: Number of deaths among persons with diabetes
Y.DM: Person-years among persons with diabetes

Details

Based on registers of the Danish population. Only included for illustrative purposes. Cannot be used as scientifically validated data, since small numbers are randomly permuted between units.

Examples

data(DMepi)
# Total deaths and person-years in the Danish population
ftable( addmargins( xtabs( cbind( Deaths=D.nD+D.DM,
                                    PYrs=Y.nD+Y.DM ) ~ P + sex,
                           data=DMepi ),
                    2 ),
        row.vars = 1 )
# Deaths and person-years in the population of diabetes patients
round(
ftable( addmargins( xtabs( cbind( Deaths=D.DM,
                                    PYrs=Y.DM ) ~ P + sex,
                           data=DMepi ),
                    2 ),
        row.vars = 1 ) )

# Model for age-specific incidence rates;
inc <- glm( X ~ sex + Ns( A, knots=seq(30,80,10) ) + P,
                offset = log(Y.nD),
                family = poisson,
                  data = DMepi )

# Predict for men and women separately in 2010:
ndm <- data.frame( sex="M", A=20:90, P=2010, Y.nD=1000 )
ndf <- data.frame( sex="F", A=20:90, P=2010, Y.nD=1000 )
prM <- ci.pred( inc, ndm )
prF <- ci.pred( inc, ndf )
matplot( ndm$A, cbind(prM,prF),
         type="l", lty=1, lwd=c(3,1,1),
         col=rep(c("blue","red"),each=3),
         log="y", xlab="Age", ylab="DM incidence per 1000 PY" )

# This is a proportional hazards model - add sex-age interaction
int <- update( inc, . ~ . + sex:Ns( A, knots=seq(30,80,10) ) )
prM <- ci.pred( int, ndm )
prF <- ci.pred( int, ndf )
matplot( ndm$A, cbind(prM,prF),
         type="l", lty=1, lwd=c(3,1,1),
         col=rep(c("blue","red"),each=3),
         log="y", xlab="Age", ylab="DM incidence per 1000 PY" )

# The rate-ratio is teased out using the ci.exp:
RRp <- ci.exp( inc, list(ndm,ndf) )
RRi <- ci.exp( int, list(ndm,ndf) )

# and added to the plot
matlines( ndm$A, cbind(RRi,RRp),
          type="l", lty=1, lwd=c(3,1,1), col=gray(rep(c(0.3,0.7),each=3)) )
abline(h=1)
axis(side=4)
mtext( "Male/Female IRR", side=4, line=2 )

Epi

Statistical Analysis in Epidemiology

v2.44

GPL-2

Authors

Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb]

Initial release

2021-02-28