wintime: Run a win time calculation

In wintime: Win Time Methods for Time-to-Event Data in Clinical Trials

Run a win time calculation

Description

This function runs one of the win time methods on observed and resampled data.

Usage

wintime(
  type,
  Time,
  Delta,
  trt,
  cov = NULL,
  model = NULL,
  resample = NULL,
  resample_num = 0,
  rmst_restriction = NA,
  seed = NA
)

Arguments

type	A string value indicating the desired win time method. Methods include 'ewt', 'ewtr', 'rmt', 'max', 'wtr', 'rwtr', and 'pwt'.
Time	A ⁠m x n⁠ matrix of event times (days), where m is the number of events in the hierarchy, and n is the total number of trial participants. Rows should represent events and columns should represent participants. Event rows should be in increasing order of clinical severity.
Delta	A ⁠m x n⁠ matrix of event indicators, where m is the number of events in the hierarchy, and n is the total number of trial participants. Rows should represent events and columns should represent participants. Event rows should be in increasing order of clinical severity.
trt	A numeric vector containing treatment arm indicators (1 for treatment, 0 for control).
cov	Optional. A ⁠n x p⁠ matrix of covariate values, where n is the total number of trial participants and p is the number of covariates. Rows should represent participants and columns should represent covariate values.
model	Optional. String value. The type of model used to calculate state distributions. Options include 'km' and 'markov'. Default depends on type.
resample	Optional. String value. The resampling method run after the observed data calculation. Options include 'boot' and 'perm'. Default depends on type.
resample_num	Optional. The number of desired resamples. Default is 0.
rmst_restriction	Required only for type = 'rmt'. The RMT cutoff time (days).
seed	Optional. Seed used for random number generation in resampling.

Value

A list containing: the observed treatment effect, a vector of length resample_num containing resampled treatment effects, a message indicating the method ran and the type of resampling done, the variance, the p-value, the total wins on treatment (pairwise methods only), the total losses on treatment (pairwise methods only). A warning message will be printed for combinations of type and model/resample that are not recommended.

Examples

# ------------------------------
# Example Inputs
# ------------------------------

# Event time vectors
TIME_1 <- c(256,44,29,186,29,80,11,380,102,33)
TIME_2 <- c(128,44,95,186,69,66,153,380,117,33)
TIME_3 <- c(435,44,95,186,69,270,1063,380,117,33)

# Event time matrix
Time <- rbind(TIME_1, TIME_2, TIME_3)

# Event indicator vectors
DELTA_1 <- c(1,0,1,0,1,1,1,0,1,0)
DELTA_2 <- c(1,0,0,0,0,1,1,0,0,0)
DELTA_3 <- c(0,0,0,0,0,0,0,0,0,0)

# Event indicator matrix
Delta <- rbind(DELTA_1, DELTA_2, DELTA_3)

# Treatment arm indicator vector
trt <- c(1,1,1,1,1,0,0,0,0,0)

# Covariate vectors
cov1 <- c(54,53,55,61,73,65,63,63,82,58,66,66)
cov2 <- c(34.4,32.1,34.7,54.1,55.7,43.6,32.1,44.8,85.2,12.5,33.4,21.4)

# Covariate vectors
cov1 <- c(66,67,54,68,77,65,55,66,77,54)
cov2 <- c(3,6,4,2,3,5,8,5,3,5)
cov3 <- c(34.6,543.6,45.8,54.7,44.3,55.6,65.9,54.7,77.9,31.2)

# Covariate matrix
cov <- cbind(cov1, cov2, cov3)

# ------------------------
# wintime Examples
# ------------------------

# Run WTR
z <- wintime("wtr", Time, Delta, trt)
print(z)

# Run EWT with default settings and 10 resamples
z <- wintime("ewt", Time, Delta, trt, resample_num = 10)
print(z)

# Run EWTR with default settings
z <- wintime("ewtr", Time, Delta, trt, cov = cov)
print(z)

# Run EWTR with KM model (This will produce a warning message)
z <- wintime("ewtr", Time, Delta, trt, cov = cov, model = "km")
print(z)

wintime documentation built on Oct. 31, 2024, 5:10 p.m.