dena

dena is an R package that aids in preprocessing, modeling, and visualization of categorical time-to-event data and egocentric social network dynamics. This readme file will show you

1. how to install and load the dena package
2. examples of how denacan be used for preprocessing nested time-to-event data
3. how to use dena’s wrapper functions to estimate competing multistate models
4. examples of how denacan be used to visualize data and model results, for example:

1. Installation

You can install the development version of dena from GitHub with:

# install.packages("devtools")
devtools::install_github("timonelmer/dena")

dena can be loaded with the following function

library(dena)

2. Preprocessing functions

When analyzing (categorical) time-to-event data of multiple participants, we enounter often data of the following format:

data(simdat2) # loading the example dataset

head(simdat2[,c(1,2,3,7,5)])
#>   id rep     time    type  Covariate1
#> 1  1   1 57.57392  friend  0.09038971
#> 2  1   2 73.51058  friend  0.34725614
#> 3  1   3 71.10374 partner -0.79411843
#> 4  1   4 61.15216 partner  0.29461076
#> 5  1   5 60.20567  family  1.23784134
#> 6  1   6 40.70458  family -0.04727337

with the meanCenteringNested function, we can mean center variables up to three nesting layers (e.g., within participants, within burst, within week).

simdat2 <- meanCenteringNested(dat = simdat2, 
                               vars = c("Covariate1","Covariate2"), 
                               nestVars = "id", 
                               verbose = F) 

head(simdat2[,c("id","Covariate1","Covariate1_id_MeanC","Covariate1_id_Mean")])
#>   id  Covariate1 Covariate1_id_MeanC Covariate1_id_Mean
#> 1  1  0.09038971         -0.03214716          0.1225369
#> 2  1  0.34725614          0.22471927          0.1225369
#> 3  1 -0.79411843         -0.91665530          0.1225369
#> 4  1  0.29461076          0.17207388          0.1225369
#> 5  1  1.23784134          1.11530447          0.1225369
#> 6  1 -0.04727337         -0.16981025          0.1225369

or we can lag variables:

simdat2 <- lagVarsNested(dat = simdat2, 
                         lags = 1,
                         vars = c("Covariate1","Covariate2"), 
                         nestVars = "id", 
                         verbose = F) 

head(simdat2[,c("id","Covariate1","Covariate1Lag1")])
#>   id  Covariate1 Covariate1Lag1
#> 1  1  0.09038971             NA
#> 2  1  0.34725614     0.09038971
#> 3  1 -0.79411843     0.34725614
#> 4  1  0.29461076    -0.79411843
#> 5  1  1.23784134     0.29461076
#> 6  1 -0.04727337     1.23784134

3. Estimation functions

The cmm function, for example, is a wrapper function for fitting multiple coxme functions with multiple categorical target states (a so-called competing risk model)

fit <- cmm(formula = Surv(time, type) ~ Covariate2 + Covariate3 + (1  |id), dat = simdat2, 
           catVar = "type", from = NULL, verbose = F)

fit[[1]] # summary of fixed effects estimates
#>          var     cat from        coef         se          z            p
#> 1 Covariate2  family NULL -0.07764620 0.07934691 -0.9785661 3.277944e-01
#> 2 Covariate3  family NULL -1.81629568 0.24592111 -7.3856842 1.516565e-13
#> 3 Covariate2  friend NULL  0.02959805 0.07655910  0.3866039 6.990494e-01
#> 4 Covariate3  friend NULL -1.75287673 0.23724507 -7.3884644 1.485478e-13
#> 5 Covariate2 partner NULL -0.01862108 0.09141615 -0.2036957 8.385913e-01
#> 6 Covariate3 partner NULL  3.57763345 0.24778702 14.4383409 0.000000e+00
#>       coef_UB    coef_LB sig
#> 1  0.07787375 -0.2331662    
#> 2 -1.33429030 -2.2983011 ***
#> 3  0.17965388 -0.1204578    
#> 4 -1.28787640 -2.2178771 ***
#> 5  0.16055458 -0.1977967    
#> 6  4.06329600  3.0919709 ***

4. Visualization functions

4.1 Describing the data visually

data(simdat)
survivalFunction(simdat, "time")

Visualizing the transitions between (interaction) states

plotTransitionNetwork(dat = simdat2, title = "ID = all", type ="sum",catVar = "type")

4.2 Visualizing model results

Visualizing coxme and coxph model estimates:

model1 <- coxme(Surv(time, event) ~ Covariate1 + Covariate2 + (1 | id), dat = simdat2)
plot.coxme(model1)

Visualizing multistate model results:

fit <- cmm(Surv(time, type) ~ Covariate2 + Covariate3 + (1 | id), dat = simdat2)
plot.cmm(fit[[1]])
#> Warning: Removed 6 rows containing missing values (geom_text).