Simulate survival data for MaxSPRT computation
simulateMaxSprtData(
n = 10000,
pExposure = 0.5,
backgroundHazard = 0.001,
tar = 10,
nullMu = 0.2,
nullSigma = 0.2,
maxT = 100,
looks = 10,
numberOfNegativeControls = 50,
numberOfPositiveControls = 1,
positiveControlEffectSize = 4
)Arguments
- n
Number of subjects.
- pExposure
Probability of being in target cohort.
- backgroundHazard
Background hazard (risk of the outcome per day).
- tar
Time at risk for each exposure
- nullMu
Null distribution mean (at log HR scale)
- nullSigma
Null distribution SD (at log HR scale)
- maxT
Maximum time to simulate.
- looks
Number of (evenly spaced) looks at the data.
- numberOfNegativeControls
Number of negative controls to simulate.
- numberOfPositiveControls
Number of positive controls to simulate.
- positiveControlEffectSize
The true effect size of the positive controls.
Value
A data frame with 5 variables:
- time
Time from index date to either the event or end of observation, whichever came first
- outcome
Whether the outcome occurred (1) or not (0)
- exposure
Whether the subject was exposed (TRUE) or not (FALSE)
- lookTime
The time point when the look occurred.
- outcomeId
A unique identifier for data corresponding to a single outcome. Lower IDs indicate negative controls, higher IDs indicate the positive control
Details
Simulate survival data for negative and positive controls. The data provides multiple looks at data accruing over time, with each look having more data than the one before. Systematic error for each outcome is drawn from the prespecified null distribution.
The outcome IDs are assigned sequentially starting at 1, with the first IDs used for the negative controls, and the latter IDs used for the positive controls.
Examples
data <- simulateMaxSprtData(n = 1000)
head(data)
#> time outcome exposure lookTime outcomeId
#> 5 8.661996 0 TRUE 10 1
#> 11 9.386998 0 TRUE 10 1
#> 20 0.498716 0 FALSE 10 1
#> 22 7.061753 0 TRUE 10 1
#> 85 3.030036 0 TRUE 10 1
#> 98 2.959496 0 TRUE 10 1