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Simulate piecewise exponential time-to-event outcomes

Source: R/pwe.R
pwe_sim.Rd

Simulates event times from a piecewise-exponential distribution, with optional administrative censoring at a fixed follow-up time.

Usage

pwe_sim(n = 1, hazard = 1, cutpoints = NULL, maxtime = NULL)

Arguments

n

A single non-negative integer giving the number of event times to simulate. The default is 1; n = 0 returns a zero-row data frame.

hazard

A numeric vector of finite, non-negative event rates, with one value per interval defined by cutpoints. The default is 1, giving a constant unit rate. If at least one outcome is requested and the final rate is zero, maxtime must be supplied so that subjects without an event can be administratively censored.

cutpoints

NULL (the default), or a numeric vector of finite, positive, strictly increasing interior times at which the hazard rate changes. The number of hazard rates must be one greater than the number of cutpoints. Use NULL for a constant hazard.

maxtime

NULL (the default), or a single finite, positive numeric administrative censoring time. When supplied, it must be later than every cutpoint.

Value

A data frame with one row per simulated subject and columns time, the event or censoring time, and event, coded 1 for an event and 0 for administrative censoring.

Details

PWEALL represents the generating hazard with pieces closed on the left and open on the right. Because the event-time distribution is continuous, the value of the hazard at an isolated cutpoint does not alter the cumulative hazard, distribution, or generated samples. When realized event times are later assigned to analysis intervals, goldilocks follows the survival counting-process convention, open on the left and closed on the right, so an event exactly at a cutpoint belongs to the interval ending there. See pwe_impute() for the conditional sampling details.

Examples

pwe_sim(10, hazard = c(0.005, 0.001), cutpoints = 3, maxtime = 36)
#>        time event
#> 1  36.00000     0
#> 2  36.00000     0
#> 3  36.00000     0
#> 4  36.00000     0
#> 5  36.00000     0
#> 6  36.00000     0
#> 7  15.53131     1
#> 8  36.00000     0
#> 9  36.00000     0
#> 10 36.00000     0
y <- pwe_sim(n = 1, hazard = c(2.585924e-02, 3.685254e-09),
             cutpoints = 12)