The poset package provides simple and efficient statistical routines for partially ordered data, such as multivariate ordinal response under consensus or prioritized order. The current version focuses on the win ratio/net benefit approach (Mao 2024) via generalized pairwise comparisons (Buyse 2010).
Installation
Install poset from CRAN with:
install.packages("poset")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("lmaowisc/poset")Examples
Here is a basic example for two-sample testing and regression.
library(poset)
## data example
head(liver)
#> R1NASH R2NASH Sex AF Steatosis SSF2 LSN
#> 1 3 2 M FALSE 30 0.21 2.33
#> 2 1 1 F FALSE 5 0.38 2.86
#> 3 4 2 M FALSE 70 0.58 3.65
#> 4 4 4 F TRUE 30 -0.08 2.73
#> 5 4 3 M TRUE 70 -0.04 2.53
#> 6 3 3 M FALSE 10 0.02 2.88Compare bivariate ratings by fibrosis stage
Y1 <- liver[liver$AF, c("R1NASH", "R2NASH")] # advanced
Y0 <- liver[!liver$AF, c("R1NASH", "R2NASH")] # not advanced
wrtest(Y1, Y0)
#> Call:
#> wrtest(Y1 = Y1, Y0 = Y0)
#>
#> Two-sample (Y1 vs Y0) win ratio/net benefit analysis
#>
#> Number of pairs: N1 x N0 = 69 x 116 = 8004
#> Win: 4251 (53.1%)
#> Loss: 2392 (29.9%)
#> Tie: 1361 (17%)
#>
#> Win ratio (95% CI): 1.78 (1.16, 2.73), p-value = 0.00856547
#> Net benefit (95% CI): 0.232 (0.065, 0.4), p-value = 0.006577537Regression analysis
Y <- 5 - liver[, c("R1NASH", "R2NASH")] # lower score is better
Z <- cbind("Female" = liver$Sex == "F",
liver[, c("AF", "Steatosis", "SSF2", "LSN")]) # covariates
obj <- wreg(Y, Z) # fit model
obj
#> Call:
#> wreg(Y = Y, Z = Z)
#>
#> n = 154 subjects with complete data
#> Comparable (win/loss) pairs: 9548/11781 = 81%
#>
#> Female AF Steatosis SSF2 LSN
#> -0.18956 -0.9660827 -0.02779146 -0.007926333 -0.1029914
summary(obj)
#> Call:
#> wreg(Y = Y, Z = Z)
#>
#> n = 154 subjects with complete data
#> Comparable (win/loss) pairs: 9548/11781 = 81%
#>
#> Newton-Raphson algoritm converged in 7 iterations
#>
#> coef exp(coef) se(coef) z Pr(>|z|)
#> Female -0.189560 0.8273 0.259988 -0.729 0.465934
#> AF -0.966083 0.3806 0.280313 -3.446 0.000568 ***
#> Steatosis -0.027791 0.9726 0.005281 -5.262 1.42e-07 ***
#> SSF2 -0.007926 0.9921 0.003953 -2.005 0.044953 *
#> LSN -0.102991 0.9021 0.125718 -0.819 0.412657
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> exp(coef) exp(-coef) lower .95 upper .95
#> Female 0.82732 1.20872 0.49702 1.3771
#> AF 0.38057 2.62763 0.21970 0.6592
#> Steatosis 0.97259 1.02818 0.96258 0.9827
#> SSF2 0.99210 1.00796 0.98445 0.9998
#> LSN 0.90213 1.10848 0.70512 1.1542
#>
#> Overall Wald test = 79.129 on 5 df, p = 1.221245e-15References
Buyse, Marc. 2010. “Generalized Pairwise Comparisons of Prioritized Outcomes in the Two-Sample Problem.” Statistics in Medicine 29 (30): 3245–57. https://doi.org/10.1002/sim.3923.
Mao, Lu. 2024. “Win Ratio for Partially Ordered Data,” Under revision.