calcWINS.formula: Win statistics calculation using formula syntax
In hce: Design and Analysis of Hierarchical Composite Endpoints

calcWINS.formula

R Documentation

Win statistics calculation using formula syntax

Description

Win statistics calculation using formula syntax

Usage

## S3 method for class 'formula'
calcWINS(x, data, ...)

Arguments

`x`	an object of class formula.
`data`	a data frame.
`...`	additional parameters.

Value

a list containing win statistics and their confidence intervals. It contains the following named data frames:

summary a data frame containing number of wins, losses, and ties of the active treatment group and the overall number of comparisons.
WP a data frame containing the win probability and its confidence interval.
NetBenefit a data frame containing the net benefit and its confidence interval. This is just a ⁠2x-1⁠ transformation of WP and its CI.
WO a data frame containing the win odds and its confidence interval.
WR1 a data frame containing the win ratio and its confidence interval, using the transformed standard error of the gamma statistic.
WR2 a data frame containing the win ratio and its confidence interval, using the standard error calculated using Pties.
gamma a data frame containing Goodman Kruskal's gamma and its confidence interval.
SE a data frame containing standard errors used to calculated the Confidence intervals for win statistics.

References

The theory of win statistics is covered in the following papers:

Win proportion and win odds confidence interval calculation:

Bamber D (1975) "The area above the ordinal dominance graph and the area below the receiver operating characteristic graph." Journal of Mathematical Psychology 12.4: 387-415. doi:10.1016/0022-2496(75)90001-2.

DeLong ER et al. (1988) "Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach." Biometrics 44.3: 837-845. doi:10.2307/2531595.

Brunner E et al. (2021) "Win odds: an adaptation of the win ratio to include ties." Statistics in Medicine 40.14: 3367-3384. doi:10.1002/sim.8967.

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.

Gasparyan SB et al. (2021) "Power and sample size calculation for the win odds test: application to an ordinal endpoint in COVID-19 trials." Journal of Biopharmaceutical Statistics 31.6: 765-787. doi:10.1080/10543406.2021.1968893.

Brunner E, Konietschke F. (2025) "An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties." Statistical Papers 66.20. doi:10.1007/s00362-024-01635-0.
Win ratio: the first CI utilizes the standard error derived from the gamma statistic standard error as outlined by:

Gasparyan SB, Kowalewski EK, Buenconsejo J, Koch GG. (2023) "Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial." In Case Studies in Innovative Clinical Trials, Chapter 7, 95–148. Chapman; Hall/CRC. doi:10.1201/9781003288640-7.
Win ratio: the second CI utilizes the standard error presented by:

Yu RX, Ganju J. (2022) "Sample size formula for a win ratio endpoint." Statistics in Medicine 41.6: 950-63. doi:10.1002/sim.9297.
Goodman Kruskal's gamma and CI: matches implementation in DescTools::GoodmanKruskalGamma() and based on:

Agresti A. (2002) Categorical Data Analysis. John Wiley & Sons, pp. 57-59. doi:10.1002/0471249688.

Brown MB, Benedetti JK. (1977) "Sampling Behavior of Tests for Correlation in Two-Way Contingency Tables." Journal of the American Statistical Association 72, 309-315. doi:10.1080/01621459.1977.10480995.

Goodman LA, Kruskal WH. (1954) "Measures of association for cross classifications." Journal of the American Statistical Association 49, 732-764. doi:10.1080/01621459.1954.10501231.

Goodman LA, Kruskal WH. (1963) "Measures of association for cross classifications III: Approximate sampling theory." Journal of the American Statistical Association 58, 310-364. doi:10.1080/01621459.1963.10500850.

Examples

# Example 1
calcWINS(x = GROUP ~ TRTP, data = COVID19b)
# Example 2
calcWINS(x = GROUP ~ TRTP, data = COVID19, ref = "Placebo", alpha = 0.01, WOnull = 1.2)
#' Example 3
calcWINS(x = GROUP ~ TRTP, data = COVID19)$WP
calcWINS(x = GROUP ~ TRTP, data = COVID19, SE_WP_Type = "unbiased")$WP

hce documentation built on April 3, 2025, 11:17 p.m.