Multiple Counting Process Structure for Survival Analysis

Counting process structure is fundamental to model time varying covariates. This package restructures dataframes in the counting process format for one or more variables. F. W. Dekker, et al. (2008) .

mtvc

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.

Installation

You can load the package as follows:

library(mtvc)

Example

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     6