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As widely discussed in biostatistics, and particularly in the field of Survival Analysis, time varying covariates should be taken into account with the counting process structure. However, in some cases, there might be models where more than one variable changes its value during the follow-up. The function mtvc takes as input one more more time varying variable, with the respective date in which that change was found, and restructures the data frame into the counting process strucure, where each patient has a time window which reflects the comorbidity status.
You can load the package as follows:
Now use mtvc function in order to restructure the data frame:
data("simwide")
#
cp.dataframe=mtvc(data=simwide,
origin='1970-01-01',
dates=c(FIRST_CHRONIC,FIRST_ACUTE,FIRST_RELAPSE),
complications=c(CHRONIC,ACUTE,RELAPSE),
start=DATETRAN,
stop=DLASTSE,
event=EVENT)
#
head(cp.dataframe[,c('id','tdep_acute','tdep_chronic','tdep_relapse','start','stop')])
#> # A tibble: 6 × 6
#> # Groups: id [3]
#> id tdep_acute tdep_chronic tdep_relapse start stop
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 0 0 26
#> 2 1 1 0 0 26 56
#> 3 1 1 1 0 56 88
#> 4 2 0 0 0 0 20
#> 5 2 0 1 0 20 533
#> 6 3 0 0 0 0 6These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.