# calcWINS.data.frame: Win statistics calculation using a data frame In hce: Design and Analysis of Hierarchical Composite Endpoints

 calcWINS.data.frame R Documentation

## Win statistics calculation using a data frame

### Description

Win statistics calculation using a data frame

### Usage

``````## S3 method for class 'data.frame'
calcWINS(x, AVAL, TRTP, 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. `ref` the reference treatment group. `alpha` 2-sided significance level. The default is 0.05. `WOnull` the null hypothesis. The default is 1. `...` 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.

• For the win proportion CI calculation see

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.

• The win odds CI is calculated using the formula in

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.

• The win ratio the first CI uses the standard error derived from the standard error of the `gamma` statistic presented in

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.

• The win ratio the second CI uses the standard error presented in

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.

• The Goodman Kruskal's `gamma` and its CI match those in `DescTools::GoodmanKruskalGamma()` and are 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.

`calcWINS()`, `calcWINS.hce()`, `calcWINS.formula()`.

### Examples

``````calcWINS(x = COVID19b, AVAL = "GROUP", TRTP = "TRTP", ref = "Placebo")
``````

hce documentation built on May 29, 2024, 5:52 a.m.