EWTP {wintime} | R Documentation |
Expected win time against trial population
Description
Calculates the combined arm state space probabilities using a Markov model or a Kaplan-Meier model (recommended). This function uses these probabilities to compare each participant's clinical state to a distribution of combined arm states.
Usage
EWTP(
n,
m,
nunique,
maxfollow,
untimes,
Time,
Delta,
dist,
markov_ind,
cov,
trt
)
Arguments
n |
The total number of trial participants. |
m |
The number of events in the hierarchy. |
nunique |
The number of unique combined arm event times (returned from wintime::markov() or wintime::km()). |
maxfollow |
The max combined arm follow up time (days) (returned from wintime::markov() or wintime::km()). |
untimes |
A vector containing unique combined arm event times (days) (returned from wintime::markov() or wintime::km()). |
Time |
A m x n matrix of event times (days). Rows should represent events and columns should represent participants. Rows should be in increasing order of clinical severity. |
Delta |
A m x n matrix of event indicators Rows should represent events and columns should represent participants. Rows should be in increasing order of clinical severity. |
dist |
A matrix of combined arm state probabilities (returned from wintime::markov() or wintime::km()). |
markov_ind |
An indicator of the model type used (1 for Markov, 0 for Kaplan-Meier). |
cov |
A n x p matrix of covariate values, where p is the number of covariates. |
trt |
A vector of length n containing treatment arm indicators (1 for treatment, 0 for control). |
Value
A list containing: The estimated treatment effect from the linear regression model, the variance, the Z-statistic, the components of the treatment effect, and the variance of the components.