S4BuyseTest-coef: Extract Summary Statistics from GPC

In BuyseTest: Generalized Pairwise Comparisons

Extract Summary Statistics from GPC

Description

Extract summary statistics (net benefit, win ratio, ...) from GPC.

Usage

## S4 method for signature 'S4BuyseTest'
coef(
  object,
  endpoint = NULL,
  statistic = NULL,
  strata = FALSE,
  cumulative = NULL,
  resampling = FALSE,
  simplify = TRUE,
  ...
)

Arguments

object	a S4BuyseTest object, output of BuyseTest.
endpoint	[character] for which endpoint(s) the summary statistic should be output? If NULL returns the summary statistic for all endpoints.
statistic	[character] the statistic summarizing the pairwise comparison: "netBenefit": displays the net benefit, as described in Buyse (2010) and Peron et al. (2016)), "winRatio": win ratio or win odds. "favorable": proportion strictly in favor of the treatment or Mann-Whitney parameter. "unfavorable": proportion in favor of the control. "neutral": proportion of neutral pairs. "uninf": proportion of uninformative pairs. "count.favorable": number of pairs in favor of the treatment. "count.unfavorable": number of pairs in favor of the control. "count.neutral": number of neutral pairs. "count.uninf": number of uninformative pairs. Default value read from BuyseTest.options().
strata	[character vector] the strata relative to which the statistic should be output. Can also be "global" or FALSE to output the statistic pooled over all strata, or TRUE to output each strata-specific statistic.
cumulative	[logical] should the summary statistic be cumulated over endpoints? Otherwise display the contribution of each endpoint.
resampling	[logical] should the summary statistic obtained by resampling be output?
simplify	[logical] should the result be coerced to the lowest possible dimension?
...	ignored.

Details

statistic: with a single endpoint denoted Y and X in the treatment and control group and a threshold of clinical relevance \tau:

• "netBenefit": P[Y \ge X + \tau] - P[X \ge Y + \tau]. See Buyse (2010).

• "winRatio": the win ratio \frac{P[Y \ge X + \tau]}{P[X \ge Y + \tau]} or the win odds \frac{P[Y \ge X + \tau]+0.5P[|Y - X|<\tau]}{P[X \ge Y + \tau]+0.5P[|Y - X|<\tau]}. see Wang (2016) and Dong (2019).

• "favorable": P[Y \ge X + \tau] or the Mann-Whitney parameter P[Y \ge X + \tau]+0.5P[|Y - X|<\tau]. See Fay (2018).

• "unfavorable": P[Y \le X + \tau] or P[Y \le X + \tau]+0.5P[|Y - X|<\tau].

The value of the argument add.halfNeutral used when running BuyseTest decides whether 0.5P[|Y - X|<\tau] is considered, e.g. whether the win ratio or win odds is output.

Value

When resampling=FALSE and simplify=FALSE, a matrix (strata, endpoint). When resampling=FALSE and simplify=FALSE, an array (sample, strata, endpoint).

Author(s)

Brice Ozenne

References

On the GPC procedure: Marc Buyse (2010). Generalized pairwise comparisons of prioritized endpoints in the two-sample problem. Statistics in Medicine 29:3245-3257
On the Mann-Whitney parameter: Fay, Michael P. et al (2018). Causal estimands and confidence intervals asscoaited with Wilcoxon-Mann-Whitney tests in randomized experiments. Statistics in Medicine 37:2923-2937
On the win odds: Dong, G., Hoaglin, D. C., Qiu, J., Matsouaka, R. A., Chang, Y. W., Wang, J., & Vandemeulebroecke, M. (2019). The Win Ratio: On Interpretation and Handling of Ties. Statistics in Biopharmaceutical Research, 12(1), 99–106. https://doi.org/10.1080/19466315.2019.1575279
On the win ratio: D. Wang, S. Pocock (2016). A win ratio approach to comparing continuous non-normal outcomes in clinical trials. Pharmaceutical Statistics 15:238-245

BuyseTest documentation built on Aug. 8, 2025, 6:32 p.m.