The Danish National Diabetes Register.

Description

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).

Usage

data(DMrand)
       data(DMlate)

Format

A data frame with 10000 observations on the following 7 variables.

sex

Sex, a factor with levels M F

dobth

Date of birth

dodm

Date of inclusion in the register

dodth

Date of death

dooad

Date of 2nd prescription of OAD

doins

Date of 2nd insulin prescription

dox

Date of exit from follow-up.

Details

All dates are given in fractions of years, so 1997.00 corresponds to 1 January 1997 and 1997.997 to 31 December 1997.

Source

Danish National Board of Health.

References

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.

Examples

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 )

Results

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> library(Epi)

Attaching package: 'Epi'

The following object is masked from 'package:base':

    merge.data.frame

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Epi/DMlate.Rd_%03d_medium.png", width=480, height=480)
> ### Name: DMlate
> ### Title: The Danish National Diabetes Register.
> ### Aliases: DMlate DMrand
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(DMlate)
> str(DMlate)
'data.frame':	10000 obs. of  7 variables:
 $ sex  : Factor w/ 2 levels "M","F": 2 1 2 2 1 2 1 1 2 1 ...
 $ dobth: num  1940 1939 1918 1965 1933 ...
 $ dodm : num  1999 2003 2005 2009 2009 ...
 $ dodth: num  NA NA NA NA NA ...
 $ dooad: num  NA 2007 NA NA NA ...
 $ doins: num  NA NA NA NA NA NA NA NA NA NA ...
 $ dox  : num  2010 2010 2010 2010 2010 ...
> 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 )
NOTE: entry.status has been set to "DM" for all.
Warning message:
In Lexis(entry = list(Per = dodm, Age = dodm - dobth, DMdur = 0),  :
  Dropping 4 rows with duration of follow up < tol

> 
> # 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 ) )
   user  system elapsed 
  1.112   0.008   1.120 
> summary( dmi )
     
Transitions:
     To
From    DM  Ins Dead Dead(Ins)  Records:  Events: Risk time:  Persons:
  DM  6157 1694 2048         0      9899     3742   45885.49      9899
  Ins    0 1340    0       451      1791      451    8387.77      1791
  Sum 6157 3034 2048       451     11690     4193   54273.27      9996
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>