hce (version 0.7.0)

regWO.data.frame: Win odds regression using a data frame

Description

Win odds regression using a data frame

Usage

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

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.

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.

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")