Generate Time-to-Event Endpoint from Piecewise Constant Exponential Distribution

This function can be used as generator to define endpoint. Implementation is based on this algorithm. This distribution can be used to simulate delayed treatment effect.

Usage

PiecewiseConstantExponentialRNG(n, risk, endpoint_name)

Arguments

n

integer. Number of random numbers

risk

a data frame of columns

end_time

End time for a constant risk in a time window. The start time of the first time window is 0.

piecewise_risk

A constant risk in a time window, which is absolute risk * relative risk, or (h0 * g) in the link.

hazard_ratio

An optional column for simulating an active arm. If absent, a column of 1s will be added. Equivalently, user can multiply piecewise_risk by hazard_ratio manually and ignore this column.

endpoint_name

character. Name of endpoint. This should be the same as the name argument when calling function endpoint().

Value

a data frame of n rows and two columns

<endpoint_name>

name of endpoint specified by users in endpoint_name.

<endpoint_name>_event

event indicator with 0/1 as censoring and event, respectively. Note that due to the nature of the algorithm to generate data from this distribution, it is possible to have the endpoint being censoring at the last end_time unless it is set to Inf.

Examples

# example code
# In this example, absolute risk in [0, 1) and [26, 52] are 0.0181 and
# 0.0027, respectively.
risk <- data.frame(
  end_time = c(1, 4.33, 26.0, 52.0),
  piecewise_risk = c(1, 1.01, 0.381, 0.150) * exp(-4.01)
)
PiecewiseConstantExponentialRNG(10, risk, 'PFS')
#>    PFS PFS_event
#> 1   52         0
#> 2   52         0
#> 3   52         0
#> 4   52         0
#> 5   52         0
#> 6   52         0
#> 7   52         0
#> 8   52         0
#> 9   52         0
#> 10  52         0