View source: R/stratWO.formula.R
| stratWO.formula | R Documentation |
Stratified win odds with adjustment using formula
## S3 method for class 'formula'
stratWO(x, data, ...)
x |
an object of class formula. |
data |
a data frame. |
... |
additional parameters. |
a data frame containing the following columns:
WO stratified (or adjusted/stratified) win odds.
LCL lower confidence limit for adjusted (or adjusted/stratified) WO.
UCL upper confidence limit for adjusted (or adjusted/stratified) WO.
SE standard error of the adjusted (or adjusted/stratified) win odds.
WOnull win odds of the null hypothesis (specified in the WOnull argument).
alpha two-sided significance level for calculating the confidence interval (specified in the alpha argument).
Pvalue p-value associated with testing the null hypothesis.
WP adjusted (or adjusted/stratified) win probability.
LCL_WP lower confidence limit for adjusted (or adjusted/stratified) WP.
UCL_WP upper confidence limit for adjusted (or adjusted/stratified) WP.
SE_WP standard error for the adjusted (or adjusted/stratified) win probability.
SD_WP standard deviation of the adjusted (or adjusted/stratified) win probability.
N total number of patients in the analysis.
Type "STRATIFIED" or "STRATIFIED/ADJUSTED" depending on whether COVAR is specified.
Gasparyan SB et al. (2021) "Adjusted win ratio with stratification: calculation methods and interpretation." Statistical Methods in Medical Research 30.2: 580-611. doi:10.1177/0962280220942558.
stratWO(), stratWO.data.frame().
# Example 1: Stratified win odds with covariate adjustment
stratWO(x = AVAL ~ TRTP + EGFRBL | STRATAN, data = KHCE)
# Example 2: Stratified win odds without covariate adjustment
stratWO(x = AVAL ~ TRTP | STRATAN, data = KHCE)
# Example 3: Only covariate adjustment (without stratification) is implemented in regWO()
regWO(x = AVAL ~ TRTP + EGFRBL, data = KHCE)
# Example 4: Non-standardized names
dat <- data.frame(response = KHCE$AVAL, treatment = KHCE$TRTP,
covariate = KHCE$EGFRBL, group = KHCE$STRATAN)
stratWO(x = response ~ treatment + covariate | group, data = dat)
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