S4BuyseTest-confint {BuyseTest}R Documentation

Confidence Intervals for Model Parameters


Computes confidence intervals for net benefit statistic or the win ratio statistic.


## S4 method for signature 'S4BuyseTest'
  statistic = NULL,
  null = NULL,
  conf.level = NULL,
  alternative = NULL,
  method.ci.resampling = NULL,
  order.Hprojection = NULL,
  transformation = NULL,
  cluster = NULL



an R object of class S4BuyseTest, i.e., output of BuyseTest


[character] the statistic summarizing the pairwise comparison: "netBenefit" displays the net benefit, as described in Buyse (2010) and Peron et al. (2016)), "winRatio" displays the win ratio, as described in Wang et al. (2016), "favorable" displays the proportion in favor of the treatment (also called Mann-Whitney parameter), as described in Fay et al. (2018). "unfavorable" displays the proportion in favor of the control. Default value read from BuyseTest.options().


[numeric] right hand side of the null hypothesis (used for the computation of the p-value).


[numeric] confidence level for the confidence intervals. Default value read from BuyseTest.options().


[character] the type of alternative hypothesis: "two.sided", "greater", or "less". Default value read from BuyseTest.options().


[character] the method used to compute the confidence intervals and p-values when using bootstrap or permutation ("percentile", "gaussian", "student"). See the details section.


[integer, 1-2] order of the H-decomposition used to compute the variance.


[logical] should the CI be computed on the logit scale / log scale for the net benefit / win ratio and backtransformed. Otherwise they are computed without any transformation. Default value read from BuyseTest.options(). Not relevant when using permutations or percentile bootstrap.


[numeric vector] Group of observations for which the iid assumption holds .


statistic: when considering a single endpoint and denoting Y the endpoint in the treatment group, X the endpoint in the control group, and τ the threshold of clinical relevance, the net benefit is P[Y ≥ X + τ] - P[X ≥ Y + τ], the win ratio is \frac{P[Y ≥ X + τ]}{P[X ≥ Y + τ]}, the proportion in favor of treatment is P[Y ≥ X + τ], the proportion in favor of control is P[X ≥ Y + τ].

method.ci.resampling: when using bootstrap/permutation, p-values and confidence intervals are computing as follow:

WARNING: when using a permutation test, the uncertainty associated with the estimator is computed under the null hypothesis. Thus the confidence interval may not be valid if the null hypothesis is false.


A matrix containing a column for the estimated statistic (over all strata), the lower bound and upper bound of the confidence intervals, and the associated p-values. When using resampling methods:


Brice Ozenne


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 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
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

See Also

BuyseTest for performing a generalized pairwise comparison.
S4BuyseTest-summary for a more detailed presentation of the S4BuyseTest object.

[Package BuyseTest version 2.3.0 Index]