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Simulate Data Set from an Illness-Death Model

Source: R/getSimulatedData.R
getSimulatedData.Rd

This function creates a single simulated data set for a single treatment arm. It simulates data from an illness-death model with one row per transition and subject.

Usage

getSimulatedData(
  N,
  transition = exponential_transition(h01 = 1, h02 = 1, h12 = 1),
  dropout = list(rate = 0, time = 12),
  accrual = list(param = "time", value = 0)
)

Arguments

N

(int)
number of patients.

transition

(TransitionParameters)
transition parameters comprising hazards, corresponding intervals and weibull_rates, see exponential_transition(), piecewise_exponential() and weibull_transition() for details.

dropout

(list)
specifies drop-out probability. Random censoring times are generated using exponential distribution. dropout$rate specifies the drop-out probability per dropout$time time units. If dropout$rate is equal to 0, then no censoring is applied.

accrual

(list)
specifies accrual intensity. See addStaggeredEntry() for details.

Value

This returns a data frame with one row per transition per individual.

Details

The output data set contains the following columns:

  • id (integer): patient id.

  • from (numeric): starting state of the transition.

  • to (character): final state of the transition.

  • entry (numeric): entry time of the transition on the individual time scale.

  • exit (numeric): exit time of the transition on the individual time scale.

  • entryAct (numeric): entry time of the transition on study time scale.

  • exitAct (numeric): exit time of the transition on study time scale.

  • censAct (numeric): censoring time of the individual on study time scale.

Examples

getSimulatedData(
  N = 10,
  transition = exponential_transition(h01 = 1, h02 = 1.5, h12 = 1),
  dropout = list(rate = 0.3, time = 1),
  accrual = list(param = "time", value = 5)
)
#>    id from to     entry        exit  entryAct   exitAct   censAct
#> 1   1    0  1 0.0000000 0.620109694 2.2084749 2.8285846  4.641584
#> 2   1    1  2 0.6201097 1.133229600 2.8285846 3.3417045  4.641584
#> 3   2    0  2 0.0000000 0.008524979 4.7083564 4.7168814  7.666176
#> 4   3    0  1 0.0000000 0.179999738 4.4569281 4.6369278 10.992647
#> 5   3    1  2 0.1799997 2.352246465 4.6369278 6.8091746 10.992647
#> 6   4    0  2 0.0000000 0.100762491 0.7384638 0.8392262  9.039251
#> 7   5    0  2 0.0000000 0.269376008 2.7693123 3.0386883  3.476619
#> 8   6    0  2 0.0000000 0.251356782 4.2223962 4.4737530  9.877679
#> 9   7    0  2 0.0000000 0.895647415 2.6340240 3.5296714  7.783116
#> 10  8    0  2 0.0000000 0.099131641 3.7638254 3.8629571  4.624288
#> 11  9    0  1 0.0000000 0.522817343 1.2393274 1.7621448  3.094538
#> 12  9    1  2 0.5228173 1.329566488 1.7621448 2.5688939  3.094538
#> 13 10    0  1 0.0000000 0.126395549 3.1352993 3.2616948 16.829769
#> 14 10    1  2 0.1263955 0.478119852 3.2616948 3.6134191 16.829769