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.
EWTP(
n,
m,
nunique,
maxfollow,
untimes,
Time,
Delta,
dist,
markov_ind,
cov,
trt,
max_time_inc
)A list containing: The estimated treatment effect from the linear regression model, the variance, the Z-statistic, the components of the treatment effect, the variance of the components, and the maximum time used in comparisons.
The total number of trial participants.
The number of events in the hierarchy.
The number of unique combined arm event times (returned from wintime::markov() or wintime::km()).
The max combined arm follow up time (days) (returned from wintime::markov() or wintime::km()).
A vector containing unique combined arm event times (days) (returned from wintime::markov() or wintime::km()).
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.
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.
A matrix of combined arm state probabilities (returned from wintime::markov() or wintime::km()).
An indicator of the model type used (1 for Markov, 0 for Kaplan-Meier).
A n x p matrix of covariate values, where p is the number of covariates.
A vector of length n containing treatment arm indicators (1 for treatment, 0 for control).
Optional. Maximal time increment for updating multi-state distribution when parametric exponential extension models are used. If unspecified, updates done at each event time in combined trial.