regWO.data.frame {hce}

Win odds regression using a data frame

Description

Win odds regression using a data frame

Usage

## S3 method for class 'data.frame'
regWO(x, AVAL, TRTP, COVAR, ref, alpha = 0.05, WOnull = 1, ...)

Arguments

x

a data frame containing subject-level data.

AVAL

variable in the data with ordinal analysis values.

TRTP

the treatment variable in the data.

COVAR

a numeric covariate.

ref

the reference treatment group.

alpha

significance level. The default is 0.05.

WOnull

the null hypothesis. The default is 1.

...

additional parameters.

Value

a data frame containing the win odds and its confidence interval.

• WO_beta adjusted win odds.

• LCL lower confidence limit for adjusted WO.

• UCL upper confidence limit for adjusted WO.

• SE standard error of the adjusted 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.

• N total number of patients in the analysis.

• beta adjusted win probability.

• SE_beta standard error for the adjusted win probability.

• SD_beta standard deviation for the adjusted win probability.

• WP (non-adjusted) win probability.

• SE_WP standard error of the non-adjusted win probability.

• SD_WP standard deviation of the non-adjusted win probability.

• WO non-adjusted win odds.

• COVAR_MEAN_DIFF mean difference between two treatment groups of the numeric covariate.

• COVAR_VAR sum of variances of two treatment groups of the numeric covariate.

• COVAR_COV covariance between the response and the numeric covariate.

References

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.

See Also

regWO().

Examples

# A baseline covariate that is highly correlated with the outcome
set.seed(2023)
dat <- COVID19
n <- nrow(dat)
dat$Severity <- ifelse(dat$GROUP > 4, rnorm(n, 0), rnorm(n, 100))
tapply(dat$Severity, dat$TRTP, mean)
regWO(x = dat, AVAL = "GROUP", TRTP = "TRTP", COVAR = "Severity", ref = "Placebo")
# Without adjustment
calcWO(x = dat, AVAL = "GROUP", TRTP = "TRTP", ref = "Placebo")