pwe_impute {goldilocks} | R Documentation |
Impute piecewise exponential time-to-event outcomes
Description
Imputation of time-to-event outcomes using the piecewise constant hazard exponential function conditional on observed exposure.
Usage
pwe_impute(time, hazard, cutpoints = 0, maxtime = NULL)
Arguments
time |
vector. The observed time for patient that have had no event or
passed |
hazard |
vector. The constant hazard rates for exponential failures. |
cutpoints |
vector. The change-point vector indicating time when the
hazard rates change. Note the first element of |
maxtime |
scalar. Maximum time before end of study. |
Details
If a subject is event-free at time s < t
, then the conditional
probability F_{T \| s}|(t \| s) = P[T \le \| T > s] = (F(t) - F(s)) /
(1 - F(s))
, where F(\cdot)
is the cumulative distribution function
of the piecewise exponential (PWE) distribution. Equivalently, F(t) =
1 - S(t)
, where S(t)
is the survival function. If U \sim
Unif(0, 1)
, then we can generate an event time (conditional on being event
free up until s
) as F^{-1}(U(1-F(s)) + F(s))
. Note: if s =
0
, then this is the equivalent of a direct (unconditional) sample from the
PWE distribution.
Value
A data frame with simulated follow-up times (time
) and
respective event indicator (event
, 1 = event occurred, 0 =
censoring).
Examples
pwe_impute(time = c(3, 4, 5), hazard = c(0.002, 0.01), cutpoints = c(0, 12))
pwe_impute(time = c(3, 4, 5), hazard = c(0.002, 0.01), cutpoints = c(0, 12),
maxtime = 36)
pwe_impute(time = 19.621870008, hazard = c(2.585924e-02, 3.685254e-09),
cutpoints = c(0, 12), maxtime = 36)