These two datasets each contain a random sample of 10,000 persons from
the Danish National Diabetes Register.
DMrand is a random sample
from the register, whereas
DMlate is a random sample among those
with date of diagnosis after 1.1.1995. All dates are radomly jittered by
adding a U(-7,7) (days).
A data frame with 10000 observations on the following 7 variables.
Sex, a factor with levels
Date of birth
Date of inclusion in the register
Date of death
Date of 2nd prescription of OAD
Date of 2nd insulin prescription
Date of exit from follow-up.
All dates are given in fractions of years, so 1997.00 corresponds to 1 January 1997 and 1997.997 to 31 December 1997.
Danish National Board of Health.
B Carstensen, JK Kristensen, P Ottosen and K Borch-Johnsen: The Danish National Diabetes Register: Trends in incidence, prevalence and mortality, Diabetologia, 51, pp 2187–2196, 2008.
In partucular see the appendix at the end of the paper.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
data(DMlate) str(DMlate) dml <- Lexis( entry=list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit=list(Per=dox), exit.status=factor(!is.na(dodth),labels=c("DM","Dead")), data=DMlate ) # Cut the follow-up at insulin start, and introduce a new timescale, # and split non-precursor states system.time( dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) ) summary( dmi )
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.