REWTPR {wintime} | R Documentation |
Time Restricted Expected win time against trial population With redistribution to the right
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. Calculation is extended by redistribution-to-the-right principles and truncated at the user-specified time_restriction (days).
Usage
REWTPR(
n,
m,
nunique2,
maxfollow2,
untimes2,
Time,
Delta,
dist2,
markov_ind,
cov,
trt,
comkm,
trans_prob2,
time_restriction,
nunique1,
maxfollow1,
untimes1,
dist1,
trtkm,
trans_prob1,
nunique0,
maxfollow0,
untimes0,
dist0,
conkm,
trans_prob0,
nimp
)
Arguments
n |
The total number of trial participants. |
m |
The number of events in the hierarchy. |
nunique2 |
The number of unique combined arm event times (returned from wintime::markov() or wintime::km()). |
maxfollow2 |
The max combined arm follow up time (days) (returned from wintime::markov() or wintime::km()). |
untimes2 |
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. |
dist2 |
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). |
comkm |
A m x nunique matrix of combined arm survival probabilities (returned from wintime::markov() or wintime::km()). |
trans_prob2 |
A (m x m x number of combined arm event times) matrix where (i,j,k)'th value is transition probability from state i to state j at k'th combined arm event time. (returned from wintime::markov() or wintime::km()). |
time_restriction |
The time restriction (days) for calculation. |
nunique1 |
The number of unique trt arm event times (returned from wintime::markov() or wintime::km()). |
maxfollow1 |
The max trt arm follow up time (days) (returned from wintime::markov() or wintime::km()). |
untimes1 |
A vector containing unique trt arm event times (days) (returned from wintime::markov() or wintime::km()). |
dist1 |
A matrix of trt arm state probabilities (returned from wintime::markov() or wintime::km()). |
trtkm |
A m x nunique matrix of trt arm survival probabilities (returned from wintime::markov() or wintime::km()). |
trans_prob1 |
A (m x m x number of trt arm event times) matrix where (i,j,k)'th value is transition probability from state i to state j at k'th trt arm event time. (returned from wintime::markov() or wintime::km()). |
nunique0 |
The number of unique control arm event times (returned from wintime::markov() or wintime::km()). |
maxfollow0 |
The max control arm follow up time (days) (returned from wintime::markov() or wintime::km()). |
untimes0 |
A vector containing unique control arm event times (days) (returned from wintime::markov() or wintime::km()). |
dist0 |
A matrix of control arm state probabilities (returned from wintime::markov() or wintime::km()). |
conkm |
A m x nunique matrix of control arm survival probabilities (returned from wintime::markov() or wintime::km()). |
trans_prob0 |
A (m x m x number of control arm event times) matrix where (i,j,k)'th value is transition probability from state i to state j at k'th control arm event time. (returned from wintime::markov() or wintime::km()). |
nimp |
The number of random imputations. |
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.