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Joint FPMs for recurrent and competing events.

Source: R/JointFPM.R
JointFPM.Rd

Fits a joint flexible parametric survival model (FPM) for a recurrent and terminal event. The joint model can be used to predict the mean number of events at different time points. This function is a wrapper around rstpm2::stpm2().

Usage

JointFPM(
  surv,
  re_model,
  ce_model,
  re_indicator,
  ce_indicator,
  df_ce = 3,
  df_re = 3,
  tvc_re_terms = NULL,
  tvc_ce_terms = NULL,
  cluster,
  data
)

Arguments

surv

A formula of the following form Surv(...) ~ 1. The Surv objects needs to be of type == 'counting' with the following arguments:

time:

Start of follow-up time for each event episode, i.e., usually 0 for the competing event and the first occurrence of the recurrent event. For every subsequent event the follow-up can either be 0 if gap time is the underlying time scale or the time of the previous event if total time is the underlying time scale.

time2:

End of follow-up, i.e., either occurrence of a terminal or recurrent event, or time of censoring.

status:

Event indicator for both terminal and recurrent event.

type:

Has to be counting.

re_model

A formula object specifying the model for the recurrent event with an empty right hand side of the formula, e.g. ~ sex.

ce_model

A formula object specifying the model for the competing event with an empty right hand side of the formula, e.g. ~ sex.

re_indicator

Indicator that defines which rows in the dataset belong to the recurrent event process. These are usually more than one row per observations. The variable name needs to be passed as a character vector.

ce_indicator

Indicator that defines which row in the dataset belong to the competing event process. The variable name needs to be passed as a character vector.

df_ce

Defines the number of knots used to model the baseline hazard function for the competing event process.

df_re

Defines the number of knots used to model the baseline hazard function for the recurrent event process.

tvc_re_terms

A named list defining the number of knots used to model potential time-varying effects of variables included in the recurrent event model. This list should be of form list(<var_name> = <no. of knots>).

tvc_ce_terms

A named list defining the number of knots used to model potential time-varying effects of variables included in the competing event model. This list should be of form list(<var_name> = <no. of knots>).

cluster

A character vector specifying the name of the variable that defines unique observations in the dataset passed to the function.

data

A stacked dataset that includes both data on the recurrent and competing event process. The dataset should have one row for each observation including the follow-up time and event indicator for the competing event and possibly multiple rows for each observation including the follow-up times and event indicator for the recurrent event, e.g.:

id st_start  st_end re status
 1      0      6.88  0      1
 1      0      6.88  1      0
 2      0      8.70  0      1
 2      0      8.70  1      0
 3      0     10     0      0
 3      0      1.78  1      1
 3      1.78   6.08  1      1
 3      6.08  10     1      0
 4      0      6.07  0      1
 4      0      6.07  1      0

Value

An object of class JointFPM with the following elements:

model:

The fitted FPM object,

re_terms:

The terms used to model the recurrent event model,

ce_terms:

The terms used to model the competing event model,

re_indicator:

The name of the indicator variable of the recurrent event

Examples

JointFPM(Surv(time  = start,
              time2 = stop,
              event = event,
              type  = 'counting') ~ 1,
         re_model = ~ pyridoxine + thiotepa,
         ce_model = ~ pyridoxine + thiotepa,
         re_indicator = "re",
         ce_indicator = "ce",
         df_ce = 3,
         df_re = 3,
         tvc_ce_terms = list(pyridoxine = 2,
                             thiotepa   = 2),
         tvc_re_terms = list(pyridoxine = 2,
                             thiotepa   = 2),
         cluster  = "id",
         data     = bladder1_stacked)
#> $model
#> 
#> Call:
#> rstpm2::stpm2(formula = model_formula, data = data, df = df_ce, 
#>     smooth.formula = bh_formula, tvc.formula = tvc_formula, cluster = data[[cluster]], 
#>     robust = TRUE)
#> 
#> Coefficients:
#>                               ce                               re 
#>                      -4.26119784                      -3.88216765 
#>                    ce:pyridoxine                      ce:thiotepa 
#>                      -0.59655478                      -0.59900162 
#>                    re:pyridoxine                      re:thiotepa 
#>                       0.16089523                       0.87325227 
#>            ce:nsx(log(stop), 3)1            ce:nsx(log(stop), 3)2 
#>                       2.00351423                       4.30723633 
#>            ce:nsx(log(stop), 3)3            re:nsx(log(stop), 3)1 
#>                       2.72222971                       3.31873438 
#>            re:nsx(log(stop), 3)2            re:nsx(log(stop), 3)3 
#>                       7.52378236                       3.28524605 
#> ce:pyridoxine:nsx(log(stop), 2)1 ce:pyridoxine:nsx(log(stop), 2)2 
#>                       1.44889533                      -0.26834877 
#>   ce:thiotepa:nsx(log(stop), 2)1   ce:thiotepa:nsx(log(stop), 2)2 
#>                       0.90885411                       1.05531401 
#> re:pyridoxine:nsx(log(stop), 2)1 re:pyridoxine:nsx(log(stop), 2)2 
#>                      -0.23787431                      -0.08296329 
#>   re:thiotepa:nsx(log(stop), 2)1   re:thiotepa:nsx(log(stop), 2)2 
#>                      -2.34781263                      -0.42711985 
#> 
#> Log-likelihood: -913.15 
#> 
#> $re_model
#> ~pyridoxine + thiotepa
#> <environment: 0x5cc35b07a520>
#> 
#> $ce_model
#> ~pyridoxine + thiotepa
#> <environment: 0x5cc35b07a520>
#> 
#> $re_indicator
#> [1] "re"
#> 
#> $ce_indicator
#> [1] "ce"
#> 
#> $cluster
#> [1] "id"
#> 
#> attr(,"class")
#> [1] "JointFPM"