Package {winratiosim}

Score Continuous Pairwise Comparisons

Description

Assigns win, loss, tie, or unresolved scores to subject pairs based on a continuous endpoint. This function is typically used after higher-priority layers have left a pair unresolved.

Usage

Scoring_Conti(dataset, higher_better, var1, var2)

Arguments

Value

A data frame matching dataset, with updated score and WR_cat columns. Scores are 1 when subject 1 wins, -1 when subject 2 wins, 0 for exact or near-exact ties, and NA when either value is missing.

Examples

pairs <- data.frame(
  usubjid1 = c(1, 1, 2),
  usubjid2 = c(3, 4, 4),
  kccq1 = c(15, 10, NA),
  kccq2 = c(10, 10, 12),
  score = NA_real_,
  WR_cat = ""
)

Scoring_Conti(pairs, higher_better = "Yes", var1 = "kccq1", var2 = "kccq2")

Score Time-to-Event Pairwise Comparisons

Description

Assigns win, loss, or unresolved scores to subject pairs based on a time-to-event endpoint. This function is typically used for the first, highest-priority layer in a hierarchical win ratio analysis.

Usage

Scoring_TTE(dataset, var1, var2, censor1, censor2)

Arguments

Value

A data frame matching dataset, with updated score and WR_cat columns. Scores are 1 when subject 1 wins, -1 when subject 2 wins, and NA when the comparison remains tied or unresolved because of censoring.

Examples

pairs <- data.frame(
  usubjid1 = c(1, 1),
  usubjid2 = c(2, 3),
  deathdays1 = c(360, 120),
  deathdays2 = c(100, 200),
  death1 = c(0, 1),
  death2 = c(1, 1),
  score = NA_real_,
  WR_cat = ""
)

Scoring_TTE(pairs, "deathdays1", "deathdays2", "death1", "death2")

Simulate Individual-Level Trial Data for One Arm

Description

Generates individual-level simulated data for a treatment or control arm in a hierarchical win ratio trial. The simulation includes frailty-adjusted time to death, recurrent event counts, censoring times, and a continuous quality-of-life change score.

Usage

SimData_per_group(
  treatment,
  ngroup,
  alpha.JFM,
  theta.JFM,
  lambda,
  ann.icr,
  censorrate,
  xbase,
  xfinal,
  sd.delta.x
)

Arguments

Value

A named list. If treatment = 1, the list contains surv_1; otherwise, it contains surv_0. The data frame has one row per subject and includes subject ID, treatment indicator, death time, censoring time, death indicator, recurrent event count, and continuous outcome value.

Examples

set.seed(1)
sim <- SimData_per_group(
  treatment = 1, ngroup = 5,
  alpha.JFM = 0, theta.JFM = 1,
  lambda = 0.13, ann.icr = 0.32,
  censorrate = 0.2, xbase = 45, xfinal = 52.5,
  sd.delta.x = 20
)
str(sim$surv_1)

Perform Hierarchical Win Ratio Analysis

Description

Analyzes treatment-control pairwise comparisons across three prioritized outcome layers. The function computes layer-specific win, tie, and loss counts; sample sizes; Finkelstein-Schoenfeld statistics; and win ratio statistics based on permutation and large-sample variance formulas.

Usage

WR_analysis(dataset1, dataset2, dataset3)

Arguments

Value

A named list with four elements:

win.losses.count.summary

Counts and proportions of treatment wins, ties, and treatment losses by layer and overall.

sample.size.summary

Treatment, control, total, and pairwise comparison counts.

FS.analysis.summary

Finkelstein-Schoenfeld statistic, variance, z-score, and one-sided p-value.

WR.analysis.summary

Win ratio, log win ratio, variance estimates, confidence limits, and one-sided p-value.

References

Finkelstein, D. M., and Schoenfeld, D. A. (1999). Combining mortality and longitudinal measures in clinical trials. Statistics in Medicine, 18(11), 1341-1354.

Pocock, S. J., Ariti, C. A., Collier, T. J., and Wang, D. (2012). The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European Heart Journal, 33(2), 176-182.

Yu, R. X., and Ganju, J. (2022). Sample size formula for a win ratio endpoint. Statistics in Medicine, 41(6), 950-963.

Examples

subjects <- data.frame(
  usubjid = c(1, 2, 1001, 1002),
  treatment = c(1, 1, 0, 0)
)
dataset1 <- merge(subjects, subjects, by = NULL)
names(dataset1) <- c("usubjid1", "treatment1", "usubjid2", "treatment2")
dataset1$score <- NA_real_
wr_rows <- dataset1$treatment1 == 1 & dataset1$treatment2 == 0
dataset1$score[wr_rows] <- c(1, 1, -1, -1)

dataset2 <- dataset1
dataset3 <- dataset1
WR_analysis(dataset1, dataset2, dataset3)$sample.size.summary

Exact Binomial Confidence Interval

Description

Computes an exact two-sided Clopper-Pearson confidence interval for a binomial proportion by inverting the binomial test.

Usage

binom.conf.exact(x, n, alpha = 0.05)

Arguments

Value

A named numeric vector with three elements:

PointEst

Observed proportion, x / n.

Lower

Lower confidence limit.

Upper

Upper confidence limit.

Examples

binom.conf.exact(x = 8, n = 10)
binom.conf.exact(x = 50, n = 100, alpha = 0.01)

Simulate Hierarchical Win Ratio Trials

Description

Simulates replicated two-arm clinical trials and analyzes each trial using a three-layer hierarchical win ratio framework: time to death, annualized recurrent event count, and a continuous quality-of-life score.

Usage

winratiosim(
  nsim,
  N,
  Randomization.ratio,
  alpha.JFM,
  theta.JFM,
  lambda_trt,
  lambda_ctl,
  ann.icr_trt,
  ann.icr_ctl,
  xbase_trt,
  xfinal_trt,
  xbase_ctl,
  xfinal_ctl,
  sd.delta.x_trt,
  sd.delta.x_ctl,
  censorrate_trt,
  censorrate_ctl,
  nc = 1,
  seed = NULL
)

Arguments

Value

A named list with the following elements:

df_FS.analysis.summary

Finkelstein-Schoenfeld analysis summary for each simulation.

df_WR.analysis.summary

Win ratio analysis summary for each simulation.

df_sample.size.summary

Sample sizes used in each simulated trial.

df_Total_probability

Win, tie, loss, and total probabilities for each simulation.

df_Total_count

Win, tie, loss, and total counts for each simulation.

References

Lee, S. Y. (2025). A note on the sample size formula for a win ratio endpoint. Statistics in Medicine, 44, e70165. doi:10.1002/sim.70165

Examples

result <- winratiosim(
  nsim = 1,
  N = 20,
  Randomization.ratio = c(1, 1),
  alpha.JFM = 0,
  theta.JFM = 1,
  lambda_trt = 0.13,
  lambda_ctl = 0.15,
  ann.icr_trt = 0.32,
  ann.icr_ctl = 0.55,
  xbase_trt = 45,
  xfinal_trt = 52.5,
  xbase_ctl = 45,
  xfinal_ctl = 45,
  sd.delta.x_trt = 20,
  sd.delta.x_ctl = 20,
  censorrate_trt = 0.2,
  censorrate_ctl = 0.2,
  nc = 1,
  seed = 2025
)
result$df_WR.analysis.summary