Type:	Package
Title:	Generalized Pairwise Comparisons
Version:	3.3.1
Date:	2025-07-07
Description:	Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
License:	GPL-3
Encoding:	UTF-8
URL:	https://github.com/bozenne/BuyseTest
BugReports:	https://github.com/bozenne/BuyseTest/issues
Depends:	R (≥ 3.5.0), Rcpp
Imports:	data.table, doSNOW, foreach, ggplot2, methods, lava, parallel, prodlim, riskRegression, rlang, scales, stats, stats4, utils
Suggests:	cvAUC, mvtnorm, pbapply, pROC, R.rsp, survival, testthat
LinkingTo:	Rcpp, RcppArmadillo
VignetteBuilder:	R.rsp
NeedsCompilation:	yes
RoxygenNote:	7.3.2
Collate:	'0-onLoad.R' '1-setGeneric.R' 'BuyseMultComp.R' 'BuyseTTEM.R' 'BuyseTest-Peron.R' 'BuyseTest-check.R' 'BuyseTest-inference.R' 'BuyseTest-initialization.R' 'BuyseTest-package.R' 'BuyseTest-print.R' 'BuyseTest.R' 'BuyseTest.options.R' 'CasinoTest.R' 'PairScore.R' 'RcppExports.R' 'S4-BuysePower.R' 'S4-BuysePower-model.tables.R' 'S4-BuysePower-nobs.R' 'S4-BuysePower-summary.R' 'S4-BuysePower-print.R' 'S4-BuysePower-show.R' 'S4-BuyseTest.R' 'S4-BuyseTest-coef.R' 'S4-BuyseTest-confint.R' 'S4-BuyseTest-get.R' 'S4-BuyseTest-model.tables.R' 'S4-BuyseTest-nobs.R' 'S4-BuyseTest-plot.R' 'S4-BuyseTest-summary.R' 'S4-BuyseTest-print.R' 'S4-BuyseTest-sensitivity.R' 'S4-BuyseTest-show.R' 'S4-BuyseTest-update.R' 'S4-BuyseTest.options.R' 'S4-BuyseTest.vcov.R' 'as.data.table.performance.R' 'auc.R' 'autoplot.S4BuyseTest.R' 'brier.R' 'constStrata.R' 'discreteRoot.R' 'doc-data.R' 'efronlim.R' 'iid.S3sensitivity.R' 'iid.prodlim.R' 'mover.R' 'normexp.R' 'performance.R' 'performanceResample.R' 'plot.S3sensitivity.R' 'powerBuyseTest.R' 'predict.logit.R' 'rbind.performanceResample.R' 'sim.simBuyseTest.R' 'simBuyseTest.R' 'simCompetingRisks.R' 'summary.performance.R' 'valid.R'
Packaged:	2025-07-07 20:02:26 UTC; bozenne
Author:	Brice Ozenne [aut, cre], Eva Cantagallo [aut], William Anderson [aut], Julien Peron [ctb], Johan Verbeeck [ctb]
Maintainer:	Brice Ozenne <brice.mh.ozenne@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-07 20:20:02 UTC

BuyseTest package: Generalized Pairwise Comparisons

Description

Implementation of the Generalized Pairwise Comparisons. BuyseTest is the main function of the package. See the vignette of an overview of the functionalities of the package. Run citation("BuyseTest") in R for how to cite this package in scientific publications. See the section reference below for examples of application in clinical studies.

The Generalized Pairwise Comparisons form all possible pairs of observations, one observation being taken from the intervention group and the other is taken from the control group, and compare the difference in endpoints (Y-X) to the threshold of clinical relevance (\tau).

For a single endpoint, if the difference is greater or equal than the threshold of clinical relevance (Y \ge X + \tau), the pair is classified as favorable (i.e. win). If the difference is lower or equal than minus the threshold of clinical relevance (X \ge Y + \tau), the pair is classified as unfavorable (i.e. loss). Otherwise the pair is classified as neutral. In presence of censoring, it might not be possible to compare the difference to the threshold. In such cases the pair is classified as uninformative.

Simultaneously analysis of several endpoints is performed by prioritizing the endpoints, assigning the highest priority to the endpoint considered the most clinically relevant. The endpoint with highest priority is analyzed first, and neutral and uninformative pair are analyzed regarding endpoint of lower priority.

Keywords: documented methods/functions are classified according to the following keywords

• models: function fitting a statistical model/method based on a dataset (e.g. auc, brier, BuyseTest, BuyseTTEM, CasinoTest, performance)

• htest: methods performing statistical inference based on an existing model (e.g. BuyseMultComp, performanceResample, powerBuyseTest, sensitivity)

• methods: extractors (e.g. getCount, getPairScore, getPseudovalue, getSurvival, getIid)

• print: concise display of an object in the console (e.g. print, summary)

• utilities: function used to facilitate user interactions (e.g. BuyseTest.options, constStrata)

• hplot: graphical display (e.g. autoplot.S3sensitivity)

• internal: function used internally but that need to be exported for parallel calculations (e.g. GPC_cpp)

• datagen: function for generating data sets (e.g. simBuyseTest, simCompetingRisks)

• classes: definition of S4 classes

Author(s)

Maintainer: Brice Ozenne brice.mh.ozenne@gmail.com (ORCID)

Authors:

• Eva Cantagallo

• William Anderson

Other contributors:

• Julien Peron [contributor]

• Johan Verbeeck [contributor]

References

Method papers on the GPC procedure and its extensions: 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 stratified win ratio: G. Dong et al. (2018). The stratified win ratio. Journal of biopharmaceutical statistics. 28(4):778-796
On the Peron's scoring rule: J. Peron, M. Buyse, B. Ozenne, L. Roche and P. Roy (2018). An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring. Statistical Methods in Medical Research 27: 1230-1239.
On the Gehan's scoring rule: Gehan EA (1965). A generalized two-sample Wilcoxon test for doubly censored data. Biometrika 52(3):650-653
On inference in GPC using the U-statistic theory: Ozenne B, Budtz-Jorgensen E, Peron J (2021). The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. Statistical Methods in Medical Research 2021 doi:10.1177/09622802211037067
On how to handle right-censoring: J. Peron, M. Idlhaj, D. Maucort-Boulch, et al. (2021) Correcting the bias of the net benefit estimator due to right-censored observations. Biometrical Journal 63: 893–906.
On how using a restriction time: Piffoux M, Ozenne B, De Backer M, Buyse M, Chiem JC, Péron J (2024). Restricted Net Treatment Benefit in oncology. Journal of Clinical Epidemiology. Jun;170:111340.

Examples of application in clinical studies:
J. Peron, P. Roy, K. Ding, W. R. Parulekar, L. Roche, M. Buyse (2015). Assessing the benefit-risk of new treatments using generalized pairwise comparisons: the case of erlotinib in pancreatic cancer. British journal of cancer 112:(6)971-976.
J. Peron, P. Roy, T. Conroy, F. Desseigne, M. Ychou, S. Gourgou-Bourgade, T. Stanbury, L. Roche, B. Ozenne, M. Buyse (2016). An assessment of the benefit-risk balance of FOLFORINOX in metastatic pancreatic adenocarcinoma. Oncotarget 7:82953-60, 2016.

Discussion about the relevance of GPC based measures of treatment effect:
J. Peron, P. Roy, B. Ozenne, L. Roche, M. Buyse (2016). The net chance of a longer survival as a patient-oriented measure of benefit in randomized clinical trials. JAMA Oncology 2:901-5.
E. D. Saad , J. R. Zalcberg, J. Peron, E. Coart, T. Burzykowski, M. Buyse (2018). Understanding and communicating measures of treatment effect on survival: can we do better?. J Natl Cancer Inst. Ezimamaka Ajufo, Aditi Nayak, Mandeep R. Mehra (2023). Fallacies of Using the Win Ratio in Cardiovascular Trials: Challenges and Solutions. JACC: Basic to Translational Science. 8(6):720-727.

See Also

Useful links:

• https://github.com/bozenne/BuyseTest

• Report bugs at https://github.com/bozenne/BuyseTest/issues

C++ Function Computing the Integral Terms for the Peron Method in the presence of competing risks (CR).

Description

Compute the integral with respect to the jump in CIF for pairs where both outcomes are censored.

Usage

.calcIntegralCif_cpp(
  cifJump,
  start_val,
  stop_val,
  cifTimeT,
  lastCIF,
  type,
  returnDeriv,
  derivSurv,
  derivSurvD
)

Arguments

Author(s)

Eva Cantagallo

C++ Function Computing the Integral Terms for the Peron Method in the survival case.

Description

Compute the integral with respect to the jump in survival for pairs where both outcomes are censored.

Usage

.calcIntegralSurv_cpp(
  survival,
  start,
  lastSurv,
  lastdSurv,
  returnDeriv,
  derivSurv,
  derivSurvD
)

Arguments

Author(s)

Brice Ozenne

Substract a vector of values in each column

Description

Fast computation of sweep(X, FUN = "-", STATS = center, MARGIN = 1)

Usage

.colCenter_cpp(X, center)

Arguments

Value

A matrix of same size as x.

Column-wise cumulative sum

Description

Fast computation of apply(x,2,cumsum)

Usage

.colCumSum_cpp(X)

Arguments

Value

A matrix of same size as x.

Multiply by a vector of values in each column

Description

Fast computation of sweep(X, FUN = "*", STATS = scale, MARGIN = 1)

Usage

.colMultiply_cpp(X, scale)

Arguments

Value

A matrix of same size as x.

Divide by a vector of values in each column

Description

Fast computation of sweep(X, FUN = "/", STATS = scale, MARGIN = 1)

Usage

.colScale_cpp(X, scale)

Arguments

Value

A matrix of same size as x.

Substract a vector of values in each row

Description

Fast computation of sweep(X, FUN = "-", STATS = center, MARGIN = 2)

Usage

.rowCenter_cpp(X, center)

Arguments

Value

A matrix of same size as x.

Apply cumprod in each row

Description

Fast computation of t(apply(x,1,cumprod))

Usage

.rowCumProd_cpp(X)

Arguments

Value

A matrix of same size as x.

Row-wise cumulative sum

Description

Fast computation of apply(x,1,cumsum)

Usage

.rowCumSum_cpp(X)

Arguments

Value

A matrix of same size as x.

Multiply by a vector of values in each row

Description

Fast computation of sweep(X, FUN = "*", STATS = center, MARGIN = 2)

Usage

.rowMultiply_cpp(X, scale)

Arguments

Value

A matrix of same size as x.

Dividy by a vector of values in each row

Description

Fast computation of sweep(X, FUN = "/", STATS = center, MARGIN = 2)

Usage

.rowScale_cpp(X, scale)

Arguments

Value

A matrix of same size as x.

Adjustment for Multiple Comparisons

Description

Adjust p-values and confidence intervals estimated via GPC for multiple comparisons.

Usage

BuyseMultComp(
  object,
  cluster = NULL,
  linfct = NULL,
  rhs = NULL,
  endpoint = NULL,
  statistic = NULL,
  cumulative = TRUE,
  conf.level = NULL,
  band = TRUE,
  global = FALSE,
  alternative = NULL,
  transformation = NULL,
  ...
)

Arguments

Details

Simulateneous confidence intervals and adjusted p-values are computed using a single-step max-test approach via the function transformCIBP of the riskRegression package. This corresponds to the single-step Dunnett described in Dmitrienko et al (2013) in table 2 and section 7.

Value

An S3 object of class BuyseMultComp.

References

Dmitrienko, A. and D'Agostino, R., Sr (2013), Traditional multiplicity adjustment methods in clinical trials. Statist. Med., 32: 5172-5218. https://doi.org/10.1002/sim.5990

Examples

#### simulate data ####
set.seed(10)
df.data <- simBuyseTest(1e2, n.strata = 3)

#### adjustment for all univariate analyses ####
ff1 <- treatment ~ TTE(eventtime, status = status, threshold = 0.1)
ff2 <- update(ff1, .~. + cont(score, threshold = 1))
BT2 <- BuyseTest(ff2, data= df.data, trace = FALSE)

## (require riskRegression >= 2021.10.04 to match)
confint(BT2, cumulative = FALSE) ## not adjusted
confintAdj <- BuyseMultComp(BT2, cumulative = FALSE, endpoint = 1:2) ## adjusted
confintAdj
if(require(lava)){
cor(lava::iid(confintAdj)) ## correlation between test-statistic
}

#### 2- adjustment for multi-arm trial ####
## case where we have more than two treatment groups
## here strata will represent the treatment groups
df.data$strata <- as.character(df.data$strata)
df.data$id <- paste0("Id",1:NROW(df.data)) ## define id variable

BT1ba <- BuyseTest(strata ~ TTE(eventtime, status = status, threshold = 1),
                   data= df.data[strata %in% c("a","b"),], trace = FALSE)
BT1ca <- BuyseTest(strata ~ TTE(eventtime, status = status, threshold = 0.1),
                   data= df.data[strata %in% c("a","c"),], trace = FALSE)
BT1cb <- BuyseTest(strata ~ TTE(eventtime, status = status, threshold = 0.1),
                   data= df.data[strata %in% c("b","c"),], trace = FALSE)
rbind("b-a" = confint(BT1ba),
      "c-a" = confint(BT1ca),
      "c-b" = confint(BT1cb)) ## not adjusted
confintAdj <- BuyseMultComp(list("b-a" = BT1ba, "c-a" = BT1ca, "c-b" = BT1cb),
                            cluster = "id", global = TRUE)
confintAdj
if(require(lava)){
cor(lava::iid(confintAdj))
}

Time to Event Model

Description

Pre-compute quantities of a time to event model useful for predictions. Only does something for prodlim objects.

Usage

BuyseTTEM(object, ...)

## S3 method for class 'formula'
BuyseTTEM(object, treatment, iid, iid.surv = "exp", ...)

## S3 method for class 'prodlim'
BuyseTTEM(object, treatment, iid, iid.surv = "exp", ...)

## S3 method for class 'survreg'
BuyseTTEM(object, treatment, n.grid = 1000, iid, ...)

## S3 method for class 'BuyseTTEM'
BuyseTTEM(object, ...)

Arguments

Value

An S3 object of class BuyseTTEM.

Examples

library(prodlim)
library(data.table)

tau <- seq(0,3,length.out=10)

#### survival case ####
set.seed(10)
df.data <- simBuyseTest(1e2, n.strata = 2)

e.prodlim <- prodlim(Hist(eventtime,status)~treatment+strata, data = df.data)
## plot(e.prodlim)

e.prodlim2 <- BuyseTTEM(e.prodlim, treatment = "treatment", iid = TRUE)

predict(e.prodlim2, time = tau, treatment = "T", strata = "a")
predict(e.prodlim, times = tau, newdata = data.frame(treatment = "T", strata = "a"))

predict(e.prodlim2, time = tau, treatment = "C", strata = "a")
predict(e.prodlim, times = tau, newdata = data.frame(treatment = "C", strata = "a"))

#### competing risk case ####
df.dataCR <- copy(df.data)
df.dataCR$status <- rbinom(NROW(df.dataCR), prob = 0.5, size = 2)

e.prodlimCR <- prodlim(Hist(eventtime,status)~treatment+strata, data = df.dataCR)
## plot(e.prodlimCR)

e.prodlimCR2 <- BuyseTTEM(e.prodlimCR, treatment = "treatment", iid = TRUE)

predict(e.prodlimCR2, time = tau, treatment = "T", strata = "a")
predict(e.prodlimCR, times = tau, newdata = data.frame(treatment = "T", strata = "a"), cause = 1)

predict(e.prodlimCR2, time = tau, treatment = "C", strata = "a")
predict(e.prodlimCR, times = tau, newdata = data.frame(treatment = "C", strata = "a"), cause = 1)

Two-group GPC

Description

Performs Generalized Pairwise Comparisons (GPC) between two groups. Can handle one or several binary, continuous and time-to-event endpoints.

Usage

BuyseTest(
  formula,
  data,
  scoring.rule = NULL,
  pool.strata = NULL,
  correction.uninf = NULL,
  model.tte = NULL,
  method.inference = NULL,
  n.resampling = NULL,
  strata.resampling = NULL,
  hierarchical = NULL,
  weightEndpoint = NULL,
  weightObs = NULL,
  neutral.as.uninf = NULL,
  add.halfNeutral = NULL,
  keep.pairScore = NULL,
  seed = NULL,
  cpus = NULL,
  trace = NULL,
  treatment = NULL,
  endpoint = NULL,
  type = NULL,
  threshold = NULL,
  status = NULL,
  operator = NULL,
  censoring = NULL,
  restriction = NULL,
  strata = NULL
)

Arguments

Details

Specification of the GPC model
There are two way to specify the GPC model in BuyseTest. A Formula interface via the argument formula with:

• on one side of the ~ symbol the endpoints by order of priority, surrounded by parentheses and a character string indicating the type of variable (binary,continuous,timetoevent). Additional argument may be included in the parenthesis: threshold of clinical relevance (threshold), restriction time for time to event endpoints (restriction), ...

• in the other side a binary variable defining the two treatment arms.

• the right-hand side of the formula may also contain a strata variable(s) surrounded by parenthese and the character strata. The argument match should be set to TRUE when strata are independent but not observations (e.g. cross-over trial with strata as patients).

A Vector interface using the following arguments

• treatment: [character] name of the treatment variable identifying the control and the experimental group. Must have only two levels (e.g. 0 and 1).

• endpoint: [character vector] the name of the endpoint variable(s).

• threshold: [numeric vector] critical values used to compare the pairs (threshold of minimal important difference). A pair will be classified as neutral if the difference in endpoint is strictly below this threshold. There must be one threshold for each endpoint variable; it must be NA for binary endpoints and positive for continuous or time to event endpoints.

• status: [character vector] the name of the binary variable(s) indicating whether the endpoint was observed or censored. Must value NA when the endpoint is not a time to event.

• operator: [character vector] the sign defining a favorable endpoint. ">0" indicates that higher values are favorable while "<0" indicates the opposite.

• type: [character vector] indicates whether it is a binary outcome ("b", "bin", or "binary"), a continuous outcome ("c", "cont", or "continuous"), or a time to event outcome ("t", "tte", "time", or "timetoevent")

• censoring: [character vector] is the endpoint subject to right or left censoring ("left" or "right"). The default is right-censoring and left-censoring is only implemented with the Gehan's scoring rule.

• restriction: [numeric vector] value above which any difference is classified as neutral.

• strata: [character vector] if not NULL, the GPC will be applied within each group of patient defined by the strata variable(s).

The formula interface can be more concise, especially when considering few outcomes, but may be more difficult to apprehend for new users. Note that arguments endpoint, threshold, status, operator, type, and censoring must have the same length.

GPC procedure
The GPC procedure form all pairs of observations, one belonging to the experimental group and the other to the control group, and class them in 4 categories:

• Favorable pair: the endpoint is better for the observation in the experimental group.

• Unfavorable pair: the endpoint is better for the observation in the control group.

• Neutral pair: the difference between the endpoints of the two observations is (in absolute value) below the threshold. When threshold=0, neutral pairs correspond to pairs with equal endpoint. Lower-priority outcomes (if any) are then used to classified the pair into favorable/unfavorable.

• Uninformative pair: censoring/missingness prevents from classifying into favorable, unfavorable or neutral.

With complete data, pairs can be decidely classified as favorable/unfavorable/neutral. In presence of missing values, the GPC procedure uses the scoring rule (argument scoring.rule) and the correction for uninformative pairs (argument correction.uninf) to classify the pairs. The classification may not be 0,1, e.g. the probability that the pair is favorable/unfavorable/neutral with the Peron's/Efron's scoring rule. To export the classification of each pair set the argument keep.pairScore to TRUE and call the function getPairScore on the result of the BuyseTest function.

Handling missing values: the recommended default approach is to use the Peron's scoring rule with a restriction time if a non-neglectable part of the survival is unknown and otherwise analyse uniformative pairs using the following endpoint(s) if any.

• scoring.rule: indicates how to handle right-censoring in time to event endpoints using information from the survival curves.

  scoring.rule="Gehan"

  When no observations is censored or only the pair with the largest timepoint is censored, the pair is decidedly classified as favorable, unfavorable, or neutral. Otherwise pairs are classified as uninformative.

  scoring.rule="Peron"

  Score pairs involving censored observations using the (group-specific) survival curves. It may still lead to uninformative pairs when the survival curve is only partially known.

  scoring.rule="Efron"

  Same as the Peron's scoring rule except that the survival curve is extrapolated to 0 when its tail is unknown. Only relevant when using a (stratified) Kaplan-Meier estimator and no competing risks.

• correction.uninf: indicates how to handle pairs that were classified as uninformative by the scoring rule.

  correction.uninf=0

  treat them as uninformative: this is an equivalent to complete case analysis when neutral.as.uninf=FALSE, while when neutral.as.uninf=TRUE, uninformative pairs are treated as neutral, i.e., analyzed at the following endpoint (if any). This approach will (generally) lead to biased estimates for the proportion of favorable, unfavorable, or neutral pairs.

  correction.uninf=1

  imputes to the uninformative pairs the average score of the informative pairs, i.e. assumes that uninformative pairs would on average behave like informative pairs. This is therefore the recommanded approach when this assumption is resonnable, typically when the the tail of the survival function estimated by the Kaplan–Meier method is close to 0.

  correction.uninf=2

  up-weight informative pairs to represent uninformative pairs. It also assumes that uninformative pairs would on average behave like informative pairs and is only recommanded when the analysis is stopped after the first endpoint with uninformative pairs.

Statistical inference
The argument method.inference defines how to approximate the distribution of the GPC estimators and so how standard errors, confidence intervals, and p-values are computed. Available methods are:

• argument method.inference="none": only the point estimate is computed which makes the execution of the BuyseTest faster than with the other methods.

• argument method.inference="u-statistic": compute the variance of the estimate using a H-projection of order 1 (default option) or 2 (see BuyseTest.options). The first order is downward biased but consistent. When considering the Gehan scoring rule, no transformation nor correction, the second order is unbiased and equivalent to the variance of the (stratified) bootstrap distribution. P-values and confidence intervals are then evaluated assuming that the estimates follow a Gaussian distribution. In presence of strata, the weights used to combine estimates across strata are assumed known. This is the case the type of weights used is pre-defined and the covariate distribution is fixed by design as in a blocked randomized trial. WARNING: the current implementation of the H-projection is not valid when using corrections for uninformative pairs (correction.uninf=1, or correction.uninf=2).

• argument method.inference="permutation": perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio).

• argument method.inference="studentized permutation": perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimate.

• argument method.inference="varExact permutation": compute the variance of the permutation distribution using a closed-form formula (Anderson and Verbeeck 2023). P-values and confidence intervals are then evaluated assuming that the estimates follow a Gaussian distribution. WARNING: the current implementation of the variance estimator for the permutation distribution is not valid when using the Peron scoring rule or corrections for uninformative pairs.

• argument method.inference="bootstrap": perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio).

• argument method.inference="studentized bootstrap": perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimator.

Additional arguments for permutation and bootstrap resampling:

• strata.resampling If NA or of length 0, the permutation/non-parametric boostrap will be performed by resampling in the whole sample. Otherwise, the permutation/non-parametric boostrap will be performed separately for each level that the variable defined in strata.resampling take.

• n.resampling set the number of permutations/samples used. A large number of permutations (e.g. n.resampling=10000) are needed to obtain accurate CI and p.value. See (Buyse et al., 2010) for more details.

• seed: the seed is used to generate one seed per sample. These seeds are the same whether one or several CPUs are used.

• cpus indicates whether the resampling procedure can be splitted on several cpus to save time. Can be set to "all" to use all available cpus. The detection of the number of cpus relies on the detectCores function from the parallel package.
  
  

Pooling results across strata
Consider K strata and denote by m_k and n_k the sample size in the control and active arm (respectively) for strata k. Let \sigma_k be the standard error of the strata-specific summary statistic (e.g. net benefit). The strata specific weights, w_k, are given by:

• "CMH": w_k=\frac{\frac{m_k \times n_k}{m_k + n_k}}{\sum_{l=1}^K \frac{m_l \times n_l}{m_l + n_l}}. Optimal if the if the odds ratios are constant across strata.

• "equal": w_k=\frac{1}{K}

• "Buyse": w_k=\frac{m_k \times n_k}{\sum_{l=1}^K m_l \times n_l}. Optimal if the risk difference is constant across strata

• "var-*" (e.g. "var-netBenefit"): . w_k=\frac{1/\sigma^2_k}{\sum_{l=1}^K 1/\sigma^2_k}

Only when using "var-winRatio", the pooled Win Ratio is computed by pooling the strata-specific win-ratios. Otherwise the pooled Win Ratio is obtained by dividing the pooled number of favorable pairs divided by the pooled number of unfavorable pairs, possibly adding half the pooled neutral pairs, according to formula (1) in Dong et al. (2018).

Default values
The default of the arguments scoring.rule, correction.uninf, method.inference, n.resampling, hierarchical, neutral.as.uninf, keep.pairScore, strata.resampling, cpus, trace is read from BuyseTest.options().
Additional (hidden) arguments are

• alternative [character] the alternative hypothesis. Must be one of "two.sided", "greater" or "less" (used by confint).

• conf.level [numeric] level for the confidence intervals (used by confint).

• keep.survival [logical] export the survival values used by the Peron's scoring rule.

• order.Hprojection [1 or 2] the order of the H-projection used to compute the variance when method.inference="u-statistic".

Value

An R object of class S4BuyseTest.

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 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 stratified win ratio: G. Dong et al. (2018). The stratified win ratio. Journal of biopharmaceutical statistics. 28(4):778-796
On the Peron's scoring rule: J. Peron, M. Buyse, B. Ozenne, L. Roche and P. Roy (2018). An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring. Statistical Methods in Medical Research 27: 1230-1239.
On the Gehan's scoring rule: Gehan EA (1965). A generalized two-sample Wilcoxon test for doubly censored data. Biometrika 52(3):650-653
On inference in GPC using the U-statistic theory: Ozenne B, Budtz-Jorgensen E, Peron J (2021). The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. Statistical Methods in Medical Research 2021 doi:10.1177/09622802211037067
On how using a restriction time: Piffoux M, Ozenne B, De Backer M, Buyse M, Chiem JC, Péron J (2024). Restricted Net Treatment Benefit in oncology. Journal of Clinical Epidemiology. Jun;170:111340.
On the argument correction.uninf: J. Peron, M. Idlhaj, D. Maucort-Boulch, et al. (2021) Correcting the bias of the net benefit estimator due to right-censored observations. Biometrical Journal 63: 893–906.
On closed-form formula for permutation variance: W.N. Anderson and J. Verbeeck (2023). Exact Permutation and Bootstrap Distribution of Generalized Pairwise Comparisons Statistics. Mathematics , 11, 1502. doi:10.3390/math11061502.

See Also

S4BuyseTest-summary for a summary of the results of generalized pairwise comparison.
S4BuyseTest-confint for exporting estimates with confidence intervals and p-values.
S4BuyseTest-model.tables for exporting the number or percentage of favorable/unfavorable/neutral/uninformative pairs.
sensitivity for performing a sensitivity analysis on the choice of the threshold(s).
S4BuyseTest-plot for graphical display of the pairs across endpoints.
getIid for exporting the first order H-decomposition.
getPairScore for exporting the scoring of each pair.

Examples

library(data.table)

#### simulate some data ####
set.seed(10)
df.data <- simBuyseTest(1e2, n.strata = 2)

## display 
if(require(prodlim)){
   resKM_tempo <- prodlim(Hist(eventtime,status)~treatment, data = df.data)
   plot(resKM_tempo)
}

#### one time to event endpoint ####
BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data= df.data)

summary(BT) ## net benefit
model.tables(BT) ## export the table at the end of summary
summary(BT, percentage = FALSE)  
summary(BT, statistic = "winRatio") ## win Ratio

## permutation instead of asymptotics to compute the p-value
## Not run: 
    BTperm <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
                    method.inference = "permutation", n.resampling = 1e3)

## End(Not run)

summary(BTperm)
summary(BTperm, statistic = "winRatio") 

## same with parallel calculations
## Not run: 
    BTperm <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
                    method.inference = "permutation", n.resampling = 1e3, cpus = 8)
    summary(BTperm)

## End(Not run)

## method Gehan is much faster but does not optimally handle censored observations
BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
                scoring.rule = "Gehan", trace = 0)
summary(BT)

#### one time to event endpoint: only differences in survival over 1 unit ####
BT <- BuyseTest(treatment ~ TTE(eventtime, threshold = 1, status = status), data=df.data)
summary(BT)

#### one time to event endpoint with a strata variable
BTS <- BuyseTest(treatment ~ strata + TTE(eventtime, status = status), data=df.data)
summary(BTS)

#### several endpoints with a strata variable
ff <- treatment ~ strata + T(eventtime, status, 1) + B(toxicity) 
ff <- update(ff, 
            ~. + T(eventtime, status, 0.5) + C(score, 1) + T(eventtime, status, 0.25))

BTM <- BuyseTest(ff, data=df.data)
summary(BTM)
plot(BTM)

#### real example : veteran dataset of the survival package ####
## Only one endpoint. Type = Time-to-event. Thresold = 0. Stratfication by histological subtype
## scoring.rule = "Gehan"

if(require(survival)){
## Not run: 
  data(cancer, package = "survival") ## import veteran
 
  ## scoring.rule = "Gehan"
  BT_Gehan <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status), 
                        data=veteran, scoring.rule="Gehan")
  
  summary_Gehan <- summary(BT_Gehan)
  summary_Gehan <- summary(BT_Gehan, statistic = "winRatio")
  
  ## scoring.rule = "Peron"
  BT_Peron <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status), 
                        data=veteran, scoring.rule="Peron")

  summary(BT_Peron)

## End(Not run)
}

Global options for BuyseTest package

Description

Update or select global options for the BuyseTest package.

Usage

BuyseTest.options(..., reinitialise = FALSE)

Arguments

Examples

library(data.table)

## see all global parameters
BuyseTest.options()

## see some of the global parameters
BuyseTest.options("n.resampling", "trace")

## update some of the global parameters
BuyseTest.options(n.resampling = 10, trace = 1)
BuyseTest.options("n.resampling", "trace")

## reinitialise all global parameters
BuyseTest.options(reinitialise = TRUE)

Class "BuyseTest.options" (global setting for the BuyseTest package)

Description

Class defining the global settings for the BuyseTest package.

Author(s)

Brice Ozenne

See Also

BuyseTest.options to select or update global settings.

Methods for the class "BuyseTest.options"

Description

Methods to update or select global settings

Usage

## S4 method for signature 'BuyseTest.options'
alloc(object, field)

## S4 method for signature 'BuyseTest.options'
select(object, name.field)

Arguments

RCT In Chronic Heart Failure Assessing an Inhibitor of the Renin-Angiotensin System.

Description

Simulated data inspired from the CHARM-Preserved Trial comparing cardiovascular death or admission to hospital for chronic heart failure (CHF) among patients with CHF treated with candesartan or placebo.

• id: study participant.

• treatment: treatment arm: candesartan (T) or placebo (C).

• Mortality: time from inclusion (say diagnosis) to death or loss to follow-up.

• statusMortality: event triggering the end of follow-up: death (1), drop-out (0).

• Hospitalization: time from inclusion (say diagnosis) to hospitalization or end of follow-up.

• statusHospitalization: hospitalization (1) or end of follow-up (0).

• Composite: time to hospitalization, death, or loss to follow-up, whichever comes first.

• statusComposite: hospitalization or death (1), drop-out (0).

Usage

data(CHARM, package = "BuyseTest")

Format

An object of class data.frame with 3023 rows and 8 columns.

Author(s)

Johan Verbeeck

References

Yusuf Salim, et al. "Effects of candesartan in patients with chronic heart failure and preserved left-ventricular ejection fraction: the CHARM-Preserved Trial". The Lancet (2003) 9386(362):777-781.

Multi-group GPC (EXPERIMENTAL)

Description

Perform Generalized Pairwise Comparisons (GPC) for two or more groups. Can handle one or several binary, continuous and time-to-event endpoints.

Usage

CasinoTest(
  formula,
  data,
  type = "unweighted",
  add.halfNeutral = NULL,
  method.inference = "u-statistic",
  conf.level = NULL,
  transformation = NULL,
  alternative = NULL,
  method.multcomp = "none",
  seed = NA
)

Arguments

Details

Require to have installed the package riskRegression and BuyseTest

Setting argument method.inference to "rank" uses a U-statistic approach with a small sample correction to match the variance estimator derived in Result 4.16 page 228 of Brunner (2018).

Value

An S3 object of class CasinoTest that inherits from data.frame.

References

Edgar Brunner, Arne C Bathke, and Frank Konietschke (2018). Rank and pseudo-rank procedures for independent observations in factorial designs. Springer.

Examples

library(data.table)
library(BuyseTest)

#### simulate data ####
set.seed(11)
n <- 4
dt <- rbind(data.table(score = rnorm(n), group = "A"),
            data.table(score = rnorm(2*n), group = "B"),
            data.table(score = rnorm(3*n), group = "C"))
dt$index <- 1:NROW(dt)

#### estimation ####
score.casino <- dt$score

## naive casino (by hand)
M.score <- outer(dt[group=="A",score],score.casino,function(x,y){x>y+0.5*(x==y)})
mean(M.score)

## naive casino (via BuyseTest)
CasinoTest(group ~ cont(score), data = dt, type = "weighted")

## harmonic casino (by hand)
hweight <- unlist(tapply(dt$group, dt$group, function(x){rep(1/length(x),length(x))}))
M.scoreW <- sweep(M.score, MARGIN = 2, FUN = "*", STATS = NROW(dt)*hweight/3)
mean(M.scoreW)

## harmonic casino (via BuyseTest)
CasinoTest(group ~ cont(score), data = dt, type = "unweighted")

#### Relative liver weights data (Brunner 2018, table 4.1, page 183) ####
liverW <- rbind(
  data.frame(value = c(3.78, 3.40, 3.29, 3.14, 3.55, 3.76, 3.23, 3.31),
             group = "Placebo"),
  data.frame(value = c(3.46,3.98,3.09,3.49,3.31,3.73,3.23),
             group = "Dose 1"),
  data.frame(value = c(3.71, 3.36, 3.38, 3.64, 3.41, 3.29, 3.61, 3.87),
             group = "Dose 2"),
  data.frame(value = c(3.86,3.80,4.14,3.62,3.95,4.12,4.54),
             group = "Dose 3"),
  data.frame(value = c(4.14,4.11,3.89,4.21,4.81,3.91,4.19, 5.05),
             group = "Dose 4")
)
liverW$valueU <- liverW$value + (1:NROW(liverW))/1e6

## same as table 4.1, page 183 in Brunner et al (2018)
CasinoTest(group ~ cont(value), data = liverW, type = "weighted", add.halfNeutral = TRUE)
CasinoTest(group ~ cont(valueU), data = liverW, type = "unweighted", add.halfNeutral = TRUE)

Rare disease trial

Description

16 pediatric subjects suffering from a aare skin disease (epidermolysis bullosa simplex) treated with placebo or diacerin cream in a longitudinal cross-over trial (14 paired).

• Id: study participant.

• Time: end of the 4 weeks treatment period (t4) or 12 weeks after treatment ended (t12)

• treatment: treatment arm: diacerin cream (V) or placebo (P).

• StdDiffCount: blister uncertainty

• Bin: indicator of 40

• DiffQoL: Change in quality of life since baseline.

• period: same as time but coded as numeric: 1 for t4 and 2 for t12

Usage

data(CHARM, package = "BuyseTest")

Format

An object of class data.frame with 30 rows and 7 columns.

References

Wally et al. "Diacerein orphan drug development for epidermolysis bullosa simplex: A phase 2/3 randomized, placebo-controlled, double-blind clinical trial". Journal of American Academy of Dermatology (2018) 78(5):892-901. https://doi.org/10.1016/j.jaad.2018.01.019.

C++ function performing the pairwise comparison over several endpoints.

Description

GPC_cpp call for each endpoint and each strata the pairwise comparison function suited to the type of endpoint and store the results.

Usage

GPC_cpp(
  endpoint,
  status,
  indexC,
  posC,
  indexT,
  posT,
  threshold,
  threshold0,
  restriction,
  weightEndpoint,
  weightObs,
  method,
  pool,
  op,
  D,
  D_UTTE,
  grid_strata,
  nUTTE_analyzedPeron_M1,
  index_endpoint,
  index_status,
  index_UTTE,
  list_survTimeC,
  list_survTimeT,
  list_survJumpC,
  list_survJumpT,
  list_lastSurv,
  p_C,
  p_T,
  iid_survJumpC,
  iid_survJumpT,
  zeroPlus,
  correctionUninf,
  hierarchical,
  hprojection,
  neutralAsUninf,
  addHalfNeutral,
  keepScore,
  precompute,
  match,
  returnIID,
  debug
)

GPC2_cpp(
  endpoint,
  status,
  indexC,
  posC,
  indexT,
  posT,
  threshold,
  threshold0,
  restriction,
  weightEndpoint,
  weightObs,
  method,
  pool,
  op,
  D,
  D_UTTE,
  grid_strata,
  nUTTE_analyzedPeron_M1,
  index_endpoint,
  index_status,
  index_UTTE,
  list_survTimeC,
  list_survTimeT,
  list_survJumpC,
  list_survJumpT,
  list_lastSurv,
  p_C,
  p_T,
  iid_survJumpC,
  iid_survJumpT,
  zeroPlus,
  correctionUninf,
  hierarchical,
  hprojection,
  neutralAsUninf,
  addHalfNeutral,
  keepScore,
  precompute,
  match,
  returnIID,
  debug
)

Arguments

Details

GPC_cpp implements GPC looping first over endpoints and then over pairs. To handle multiple endpoints, it stores some of the results which can be memory demanding when considering large sample - especially when computing the iid decomposition. GPC2_cpp implements GPC looping first over pairs and then over endpoints. It has rather minimal memory requirement but does not handle correction for uninformative pairs.

Author(s)

Brice Ozenne

Class "S4BuysePower" (output of BuyseTest)

Description

A powerBuyseTest output is reported in a S4BuysePower object.

Author(s)

Brice Ozenne

See Also

powerBuyseTest for the function computing generalized pairwise comparisons.
S4BuysePower-summary for the summary of the BuyseTest function results

Extract Summary for Class "S4BuysePower"

Description

Extract a summary of the results from the powerBuyseTest function.

Usage

## S4 method for signature 'S4BuysePower'
model.tables(
  x,
  type = "summary",
  statistic = NULL,
  endpoint = NULL,
  order.Hprojection = NULL,
  transformation = NULL
)

Arguments

Value

data.frame

Author(s)

Brice Ozenne

See Also

powerBuyseTest for performing a simulation study for generalized pairwise comparison.

Sample Size for Class "S4BuysePower"

Description

Display the sample size in each treatmnet arm as well as the number of pairs.

Usage

## S4 method for signature 'S4BuysePower'
nobs(object, ...)

Arguments

Value

A data.frame with two colunms, one for each treatment group, and as many rows as sample sizes used for the simulation.

Author(s)

Brice Ozenne

Print Method for Class "S4BuysePower"

Description

Display the main results stored in a S4BuysePower object.

Usage

## S4 method for signature 'S4BuysePower'
print(x, ...)

Arguments

Value

invisible table

Author(s)

Brice Ozenne

See Also

powerBuyseTest for performing power calculation based on GPC.
S4BuysePower-summary for a more detailed presentation of the S4BuysePower object.

Show Method for Class "S4BuysePower"

Description

Display the main results stored in a S4BuysePower object.

Display the main results stored in a S4BuyseTest object.

Usage

## S4 method for signature 'S4BuysePower'
show(object)

## S4 method for signature 'S4BuyseTest'
show(object)

Arguments

Value

invisible NULL

Author(s)

Brice Ozenne

See Also

powerBuyseTest for performing power calculation based on GPC.
S4BuysePower-summary for a more detailed presentation of the S4BuysePower object.

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

Summary Method for Class "S4BuysePower"

Description

Summarize the results from the powerBuyseTest function.

Usage

## S4 method for signature 'S4BuysePower'
summary(
  object,
  statistic = NULL,
  endpoint = NULL,
  order.Hprojection = NULL,
  transformation = NULL,
  print = TRUE,
  legend = TRUE,
  col.rep = FALSE,
  digit = 4,
  ...
)

Arguments

Value

data.frame

Author(s)

Brice Ozenne

See Also

powerBuyseTest for performing a simulation study for generalized pairwise comparison.

Class "S4BuyseTest" (output of BuyseTest)

Description

A BuyseTest output is reported in a S4BuyseTest object.

Author(s)

Brice Ozenne

See Also

BuyseTest for the function computing generalized pairwise comparisons.
S4BuyseTest-summary for the summary of the BuyseTest function results

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

Details

One of the following statistic can be specified:

• "netBenefit": returns the net benefit.

• "winRatio": returns the win ratio.

• "favorable": returns the proportion in favor of the treatment (also called Mann-Whitney parameter).

• "unfavorable": returns the proportion in favor of the control.

• "unfavorable": returns the proportion of neutral pairs.

• "unfavorable": returns the proportion of uninformative pairs.

• "count.favorable": returns the number of pairs in favor of the treatment.

• "count.unfavorable": returns the number of pairs in favor of the control.

• "count.neutral": returns the number of neutral pairs.

• "count.uninf": returns the number of uninformative pairs.

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

Extract Confidence Interval from GPC

Description

Extract confidence intervals for summary statistics (net benefit, win ratio, ...) estimated by GPC.

Usage

## S4 method for signature 'S4BuyseTest'
confint(
  object,
  endpoint = NULL,
  statistic = NULL,
  strata = FALSE,
  cumulative = TRUE,
  null = NULL,
  conf.level = NULL,
  alternative = NULL,
  method.ci.resampling = NULL,
  order.Hprojection = NULL,
  transformation = NULL,
  cluster = NULL,
  sep = "."
)

Arguments

Details

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

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

• percentile (bootstrap): compute the confidence interval using the quantiles of the bootstrap estimates. Compute the p-value by finding the confidence level at which a bound of the confidence interval equals the null hypothesis.

• percentile (permutation): apply the selected transformation to the estimate and permutation estimates. Compute the confidence interval by (i) shfiting the estimate by the quantiles of the centered permutation estimates and (ii) back-transforming . Compute the p-value as the relative frequency at which the estimate are less extreme than the permutation estimates.

• gaussian (bootstrap and permutation): apply the selected transformation to the estimate and bootstrap/permutation estimates. Estimate the variance of the estimator using the empirical variance of the transformed boostrap/permutation estimates. Compute confidence intervals and p-values under the normality assumption and back-transform the confidence intervals.

• student (bootstrap): apply the selected transformation to the estimate, its standard error, the bootstrap estimates, and their standard error. Compute the studentized bootstrap estimates by dividing the centered bootstrap estimates by their standard error. Compute the confidence interval based on the standard error of the estimate and the quantiles of the studentized bootstrap estimates, and back-transform. Compute the p-value by finding the confidence level at which a bound of the confidence interval equals the null hypothesis.

• student (permutation): apply the selected transformation to the estimate, its standard error, the permutation estimates, and their standard error. Compute the studentized permutation estimates by dividing the centered permutation estimates by their standard error. Compute the confidence interval based on the standard error of the estimate and the quantiles of the studentized permutation estimates, and back-transform. Compute the p-value as the relative frequency at which the studentized estimate are less extreme than the permutation studentized estimates.

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.

Value

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:

• an attribute n.resampling specified how many samples have been used to compute the confidence intervals and the p-values.

• an attribute method.ci.resampling method used to compute the confidence intervals and p-values.

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 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.

Extract Summary for Class "S4BuyseTest"

Description

Extract a summary of the results from the BuyseTest function.

Usage

## S4 method for signature 'S4BuyseTest'
model.tables(
  x,
  percentage = TRUE,
  statistic = NULL,
  conf.level = NULL,
  strata = NULL,
  columns = "summary",
  ...
)

Arguments

Author(s)

Brice Ozenne

See Also

BuyseTest for performing a generalized pairwise comparison.
S4BuyseTest-class for a presentation of the S4BuyseTest object.
S4BuyseTest-confint to output confidence interval and p-values in a matrix format.

Examples

library(data.table)

dt <- simBuyseTest(1e2, n.strata = 3)

 ## Not run: 
 BT <- BuyseTest(treatment ~ TTE(eventtime, status = status) + Bin(toxicity), data=dt)
 
## End(Not run)
 
 model.tables(BT)
 model.tables(BT, percentage = FALSE)
 model.tables(BT, statistic = "winRatio")

Sample Size for Class "S4BuyseTest"

Description

Display the sample size in each treatment arm as well as the number of pairs.

Usage

## S4 method for signature 'S4BuyseTest'
nobs(object, strata = FALSE, resampling = FALSE, simplify = TRUE, ...)

Arguments

Value

A vector (when argument strata is FALSE) or a matrix (when argument strata is TRUE). In the latter case each line correspond to a strata.

Author(s)

Brice Ozenne

Graphical Display for GPC

Description

Graphical display of the percentage of favorable, unfavorable, neutral, and uninformative pairs per endpoint.

Usage

## S4 method for signature 'S4BuyseTest,ANY'
plot(
  x,
  type = "hist",
  strata = "global",
  endpoint = NULL,
  label.strata = NULL,
  label.endpoint = NULL,
  plot = TRUE,
  color = c("#7CAE00", "#F8766D", "#C77CFF", "#00BFC4"),
  ...
)

Arguments

Value

an invisible list containing the data and the ggplot object used for graphical display.

Examples

if(require(ggplot2)){

## simulate data
set.seed(10)
df.data <- simBuyseTest(1e2, n.strata = 2)

ff1 <- treatment ~ bin(toxicity) + TTE(eventtime, status = status,
                                      restriction = 1, threshold = 0.5)
BT1 <- BuyseTest(ff1, data= df.data)
plot(BT1, type = "hist")
plot(BT1, type = "pie")
plot(BT1, type = "racetrack")

ff2 <- update(ff1, ~.+cont(score))
BT2 <- BuyseTest(ff2, data= df.data)
plot(BT2, type = "hist")
plot(BT2, type = "pie")
plot(BT2, type = "racetrack")

}

Print Method for Class "S4BuyseTest"

Description

Display the main results stored in a S4BuyseTest object.

Usage

## S4 method for signature 'S4BuyseTest'
print(x, ...)

Arguments

Author(s)

Brice Ozenne

See Also

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

Summary Method for Class "S4BuyseTest"

Description

Summarize the results from the BuyseTest function.

Usage

## S4 method for signature 'S4BuyseTest'
summary(
  object,
  print = TRUE,
  percentage = TRUE,
  statistic = NULL,
  conf.level = NULL,
  strata = NULL,
  type.display = 1,
  digit = c(2, 4, 5),
  ...
)

Arguments

Details

Content of the output
The "results" table in the output show the result of the GPC at each endpoint, as well as its contribution to the global statistics. More precisely, the column:

• endpoint lists the endpoints, by order of priority.

• threshold lists the threshold associated to each endpoint.

• weight: lists the weight of each priority.

• strata: list the strata relative to which the results of the priority are displayed. If "global", then the results are over all strata at a given priority.

• total (or total(%)) lists the number (or percentage) of pairs to be analyzed at the current priority (or strata).

• favorable (or favorable(%)) lists the number (or percentage) of pairs classified in favor of the treatment group at the current priority (or strata).

• unfavorable (or unfavorable(%)) lists the number (or percentage) of pairs classified in favor of the control group at the current priority (or strata).

• neutral (or neutral(%)) lists the number (or percentage) of pairs classified as neither in favor of the treatment group nor in favor of the control group at the current priority (or strata).

• uninf (or uninf(%)) lists the number (or percentage) of pairs that could not be classified at the current priority (or strata) due to missing values/censoring.

• delta lists the value of the priority-specific statistic (e.g. net benefit or win ratio), i.e. computed on the pairs analyzed at the current priority only.

• Delta lists the value of the cumulative statistic (e.g. net benefit or win ratio), i.e. computed on all the pairs analyzed up to the current priority.

• Delta(%) lists the relative statistic (i.e. statistic up to the current priority divided by the final statistic).

• information(%) lists the information fraction (i.e. number of favorable and unfavorable pairs up to the current priority divided by the final number of favorable and unfavorable pairs).

• CI lists the confidence intervals for Delta (not adjusted for multiple comparison).

• null lists the null hypothesis (Delta=null).

• p.value p-value relative to the null hypothesis (not adjusted for multiple comparison).

• resampling number of samples used to compute the confidence intervals or p-values from permutations or bootstrap samples. Only displayed if some bootstrap samples have been discarded, for example, they did not lead to sample any case or control.

Note: when using the Peron scoring rule or a correction for uninformative pairs, the columns total, favorable, unfavorable, neutral, and uninf are computing by summing the contribution of the pairs. This may lead to a decimal value.

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

Statistical inference
When the interest is in obtaining p-values, we recommand the use of a permutation test. However, when using a permutation test confidence intervals are not displayed in the summary. This is because there is no (to the best of our knowledge) straightforward way to obtain good confidence intervals with permutations. An easy way consist in using the quantiles of the permutation distribution and then shift by the point estimate of the statistic. This is what is output by S4BuyseTest-confint. However this approach leads to a much too high coverage when the null hypothesis is false. The limits of the confidence interval can also end up being outside of the interval of definition of the statistic (e.g. outside [-1,1] for the proportion in favor of treatment). Therefore, for obtaining confidence intervals, we recommand the boostrap method or the u-statistic method.

Win ratio
For the win ratio, the proposed implementation enables the use of thresholds and endpoints that are not time to events as well as the correction proposed in Peron et al. (2016) to account for censoring. These development have not been examined by Wang et al. (2016), or in other papers (to the best of our knowledge). They are only provided here by implementation convenience.

Competing risks
In presence of competing risks, looking at the net benefit/win ratio computed with respect to the event of interest will likely not give a full picture of the difference between the two groups. For instance a treatment may decrease the risk of the event of interest (i.e. increase the net benefit for this event) by increasing the risk of the competing event. If the competing event is death, this is not desirable. It is therefore advised to taking into consideration the risk of the competing event, e.g. by re-running BuyseTest where cause 1 and 2 have been inverted.

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 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-model.tables to obtain the table displayed at the end of the summary method in a data.frame format. S4BuyseTest-confint to output estimate, standard errors, confidence interval and p-values. S4BuyseTest-plot for a graphical display of the scoring of the pairs. BuyseMultComp for efficient adjustment for multiple comparisons.

Examples

library(data.table)

dt <- simBuyseTest(1e2, n.strata = 3)

 ## Not run: 
 BT <- BuyseTest(treatment ~ TTE(eventtime, status = status) + Bin(toxicity), data=dt)
 
## End(Not run)
 
 summary(BT)
 summary(BT, percentage = FALSE)
 summary(BT, statistic = "winRatio")

Re-run two-group GPC

Description

Re-run Generalized Pairwise Comparisons (GPC) between two groups.

Usage

## S4 method for signature 'S4BuyseTest'
update(object, ...)

Arguments

Value

an R object of class S4BuyseTest

Author(s)

Brice Ozenne

Extract Uncertainty from GPC

Description

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

Usage

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

Arguments

Details

When consider a second order H-decomposition, it is only used to estimate variance: the correlation between endpoints is evaluated using the first order H-decomposition.

Value

A numeric matrix.

Author(s)

Brice Ozenne

Convert Performance Objet to data.table

Description

Extract the AUC/brier score values or the prediction into a data.table format.

Usage

## S3 method for class 'performance'
as.data.table(
  x,
  keep.rownames = FALSE,
  type = "performance",
  format = NULL,
  ...
)

Arguments

Value

A data.table object

Estimation of the Area Under the ROC Curve (EXPERIMENTAL)

Description

Estimation of the Area Under the ROC curve, possibly after cross validation, to assess the discriminant ability of a biomarker regarding a disease status.

Usage

auc(
  labels,
  predictions,
  fold = NULL,
  observation = NULL,
  direction = ">",
  add.halfNeutral = TRUE,
  null = 0.5,
  conf.level = 0.95,
  transformation = TRUE,
  order.Hprojection = 2,
  pooling = "mean"
)

Arguments

Details

The iid decomposition of the AUC is based on a first order decomposition. So its squared value will not exactly match the square of the standard error estimated with a second order H-projection.

Value

An S3 object of class BuyseTestAUC that inherits from data.frame. The last line of the object contains the global AUC value with its standard error.

References

Erin LeDell, Maya Petersen, and Mark van der Laan (2015). Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron J Stat. 9(1):1583–1607.

Examples

library(data.table)

n <- 200
set.seed(10)
X <- rnorm(n)
dt <- data.table(Y = as.factor(rbinom(n, size = 1, prob = 1/(1+exp(1/2-X)))),
                 X = X,
                 fold = unlist(lapply(1:10,function(iL){rep(iL,n/10)})))

## compute auc
auc(labels = dt$Y, predictions = dt$X, direction = ">")

## compute auc after 10-fold cross-validation
auc(labels = dt$Y, prediction = dt$X, fold = dt$fold, observation = 1:NROW(dt))

Graphical Display for GPC

Description

Graphical display of the percentage of favorable, unfavorable, neutral, and uninformative pairs per endpoint.

Usage

## S3 method for class 'S4BuyseTest'
autoplot(
  object,
  type = "hist",
  strata = "global",
  endpoint = NULL,
  label.strata = NULL,
  label.endpoint = NULL,
  color = c("#7CAE00", "#F8766D", "#C77CFF", "#00BFC4"),
  ...
)

Arguments

Value

a ggplot object.

Estimation of the Brier Score (EXPERIMENTAL)

Description

Estimation of the brier score, possibly after cross validation, to assess the discriminant ability and calibration of a biomarker regarding a disease status.

Usage

brier(
  labels,
  predictions,
  iid = NULL,
  fold = NULL,
  observation = NULL,
  null = NA,
  conf.level = 0.95,
  transformation = TRUE
)

Arguments

Value

An S3 object of class BuyseTestBrier that inherits from data.frame.

C++ Function pre-computing the Integral Terms for the Peron Method in the survival case.

Description

Compute the integral with respect to the jump in survival for pairs where both outcomes are censored, i.e. \int S1(t+\tau) dS2(t).

Usage

calcIntegralSurv2_cpp(
  time,
  survival,
  dSurvival,
  index_survival,
  index_dSurvival1,
  index_dSurvival2,
  lastSurv,
  lastdSurv,
  iidNuisance,
  nJump
)

Arguments

Author(s)

Brice Ozenne

Extract the AUC Value

Description

Extract the AUC value.

Usage

## S3 method for class 'BuyseTestAuc'
coef(object, ...)

Arguments

Value

Estimated value for the AUC (numeric).

Extract the Brier Score

Description

Extract the Brier score.

Usage

## S3 method for class 'BuyseTestBrier'
coef(object, ...)

Arguments

Value

Estimated value for Brier score (numeric).

Extract the AUC value with its Confidence Interval

Description

Extract the AUC value with its Confidence Interval and p-value testing whether the AUC equals 0.5.

Usage

## S3 method for class 'BuyseTestAuc'
confint(object, ...)

Arguments

Value

Estimated value for the AUC, its standard error, the lower and upper bound of the confidence interval and the p-value.

Extract the Brier Score with its Confidence Interval

Description

Extract the Brier score with its Confidence Interval and possibly a p-value.

Usage

## S3 method for class 'BuyseTestBrier'
confint(object, ...)

Arguments

Value

Estimated value for the brier score, its standard error, the lower and upper bound of the confidence interval and the p-value.

Strata creation

Description

Create strata from several variables.

Usage

constStrata(
  data,
  strata,
  sep = ".",
  lex.order = FALSE,
  trace = TRUE,
  as.numeric = FALSE
)

Arguments

Details

This function uses the interaction function from the base package to form the strata.

Value

A factor vector or a numeric vector.

Author(s)

Brice Ozenne

Examples

library(data.table)

library(survival) ## import veteran
  
# strata with two variables : celltype and karno
veteran$strata1 <- constStrata(veteran,c("celltype","karno"))
table(veteran$strata1)
  
# strata with three variables : celltype, karno and age dichotomized at 60 years
veteran$age60 <- veteran$age>60
veteran$age60 <- factor(veteran$age60,labels=c("<=60",">60")) # convert to factor with labels
veteran$strata2 <- constStrata(veteran,c("celltype","karno","age60"))
table(veteran$strata2) # factor strata variable 
  
veteran$strata2 <- constStrata(veteran,c("celltype","karno","age60"), as.numeric=TRUE)
table(veteran$strata2) # numeric strata variable

Constrained Kaplan-Meier Estimator

Description

Kaplan-Meier estimator, possibly stratified, constrained to 0 just after end of follow-up in each strata.

Usage

efronlim(formula, data, ...)

Arguments

Details

If in any strata there is censoring at the observed time, then the dataset is updated by setting one of the censored observations to an infinitesimal later timepoint with an event instead of censoring. Then the possibly stratified Kaplan-Meier estimator is run on this updated dataset.

Value

A prodlim object.

Examples

library(data.table)

set.seed(1)
dt.data <- simBuyseTest(1e2, n.strata = 2)
dt.data$time1 <- pmin(dt.data$eventtime, 1)
dt.data$status1 <- dt.data$status * (dt.data$eventtime<=1)

## KM
if(require(prodlim)){
   e.KM <- prodlim(Hist(time1,status1)~1, data = dt.data)
   plot(e.KM)
}

e.KM0 <- efronlim(Hist(time1,status1)~1, data = dt.data)
plot(e.KM0)

## stratfied KM
if(require(prodlim)){
   e.KMS <- prodlim(Hist(time1,status1)~strata, data = dt.data)
   plot(e.KMS)
}

e.KMS <- efronlim(Hist(time1,status1)~strata, data = dt.data)
plot(e.KMS)

Extract the Number of Favorable, Unfavorable, Neutral, Uninformative pairs

Description

Extract the number of favorable, unfavorable, neutral, uninformative pairs.

Usage

## S4 method for signature 'S4BuyseTest'
getCount(object, type)

Arguments

Value

A "vector" containing the number of pairs

Author(s)

Brice Ozenne

Extract the H-decomposition of the Estimator

Description

Extract the H-decomposition of the GPC estimator.

Usage

## S4 method for signature 'S4BuyseTest'
getIid(
  object,
  endpoint = NULL,
  statistic = NULL,
  strata = FALSE,
  cumulative = TRUE,
  center = TRUE,
  scale = TRUE,
  type = "all",
  cluster = NULL,
  simplify = TRUE
)

Arguments

Details

WARNING: argument scale and center should be used with care as when set to FALSE they may not lead to a meaningful decomposition.

Value

A list of matrices, each element of the list correspond to a statistic (global or strata-specific) and each matrix has as many columns as endpoints and rows as observations.

Author(s)

Brice Ozenne

See Also

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

Extract the Score of Each Pair

Description

Extract the score of each pair.

Usage

## S4 method for signature 'S4BuyseTest'
getPairScore(
  object,
  endpoint = NULL,
  strata = NULL,
  cumulative = FALSE,
  rm.withinStrata = TRUE,
  rm.strata = is.na(object@strata),
  rm.indexPair = TRUE,
  rm.weight = FALSE,
  rm.corrected = (object@correction.uninf == 0),
  unlist = TRUE,
  trace = 1
)

Arguments

Details

The maximal output (i.e. with all columns) contains for each endpoint, a data.table with:

• "strata": the name of the strata to which the pair belongs.

• "index.T": the index of the treatment observation in the pair relative to the original dataset.

• "index.C": the index of the control observation in the pair relative to the original dataset.

• "indexWithinStrata.T": the index of the treatment observation in the pair relative to the treatment group and the strata.

• "indexWithinStrata.C": the index of the control observation in the pair relative to the control group and the strata.

• "favorable": the probability that the endpoint is better in the treatment arm vs. in the control arm.

• "unfavorable": the probability that the endpoint is worse in the treatment arm vs. in the control arm.

• "neutral": the probability that the endpoint is no different in the treatment arm vs. in the control arm.

• "uninformative": the weight of the pair that cannot be attributed to favorable/unfavorable/neutral.

• "weight": the residual weight of the pair to be analyzed at the current outcome. Each pair starts with a weight of 1.

• "favorable.corrected": same as "favorable" after weighting.

• "unfavorable.corrected": same as "favorable" after weighting.

• "neutral.corrected": same as "favorable" after weighting.

• "uninformative.corrected": same as "favorable" after weighting.

Note that the .T and .C may change since they correspond of the label of the treatment and control arms. The first weighting consists in multiplying the probability by the residual weight of the pair (i.e. the weight of the pair that was not informative at the previous endpoint). This is always performed. For time to event endpoint an additional weighting may be performed to avoid a possible bias in presence of censoring.

Author(s)

Brice Ozenne

Examples

library(data.table)
library(prodlim)

## run BuyseTest
library(survival) ## import veteran

BT.keep <- BuyseTest(trt ~ tte(time, threshold = 20, status = "status") + cont(karno),
                     data = veteran, keep.pairScore = TRUE, 
                     trace = 0, method.inference = "none")

## Extract scores
pScore <- getPairScore(BT.keep, endpoint = 1)

## look at one pair
indexPair <- intersect(which(pScore$index.1 == 22),
                       which(pScore$index.2 == 71))
pScore[indexPair]

## retrive pair in the original dataset
pVeteran <- veteran[pScore[indexPair,c(index.1,index.2)],]
pVeteran

## the observation from the control group is censored at 97
## the observation from the treatment group has an event at 112
## since the threshold is 20, and (112-20)<97
## we know that the pair is not in favor of the treatment

## the formula for probability in favor of the control is
## Sc(97)/Sc(112+20)
## where Sc(t) is the survival at time t in the control arm.

## we first estimate the survival in each arm
e.KM <- prodlim(Hist(time,status)~trt, data = veteran)

## and compute the survival
iSurv <- predict(e.KM, times =  c(97,112+20),
                 newdata = data.frame(trt = 1, stringsAsFactors = FALSE))[[1]]

## the probability in favor of the control is then
pUF <- iSurv[2]/iSurv[1]
pUF
## and the complement to one of that is the probability of being neutral
pN <- 1 - pUF
pN

if(require(testthat)){
   testthat::expect_equal(pUF, pScore[indexPair, unfavorable])
   testthat::expect_equal(pN, pScore[indexPair, neutral])
}

Extract the pseudovalues of the Estimator

Description

Extract the pseudovalues of the estimator. The average of the pseudovalues is the estimate and their standard deviation the standard error of the estimate times a factor n (i.e. a t-test on their mean will give asymptotically valid confidence intervals and p-values).

Usage

## S4 method for signature 'S4BuyseTest'
getPseudovalue(object, statistic = NULL, endpoint = NULL)

Arguments

Author(s)

Brice Ozenne

See Also

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

Examples

set.seed(10)
n <- 250
d <- simBuyseTest(n)

e.BT <- BuyseTest(treatment ~ tte(eventtime,status,2) + bin(toxicity),
                 data = d, trace = 0)

#### net Benefit
pseudo <- getPseudovalue(e.BT)
summary(lm(pseudo~1))$coef
## asymptotically equivalent to
confint(e.BT, transformation = TRUE)
## (small differences: small sample corrections)

summary(lm(getPseudovalue(e.BT, endpoint = 1)~1))$coef

#### win Ratio
pseudo <- getPseudovalue(e.BT, statistic = "winRatio")
summary(lm(pseudo~1))$coef ## wrong p-value (should compare to 1 instead of 0)
## asymptotically equivalent to
confint(e.BT, statistic = "winRatio", transformation = TRUE)

#### favorable
pseudo <- getPseudovalue(e.BT, statistic = "favorable")
summary(lm(pseudo~1))$coef ## wrong p-value (should compare to 1/2 instead of 0)
## asymptotically equivalent to
confint(e.BT, statistic = "favorable", transformation = TRUE)

#### unfavorable
pseudo <- getPseudovalue(e.BT, statistic = "unfavorable")
summary(lm(pseudo~1))$coef ## wrong p-value (should compare to 1/2 instead of 0)
## asymptotically equivalent to
confint(e.BT, statistic = "unfavorable", transformation = TRUE)

Extract the Survival and Survival Jumps

Description

Extract the survival and survival jumps.

Usage

## S4 method for signature 'S4BuyseTest'
getSurvival(
  object,
  type = NULL,
  endpoint = NULL,
  strata = NULL,
  unlist = TRUE,
  trace = TRUE
)

Arguments

Details

The argument type can take any of the following values:

• "survTimeC": survival at the event times for the observations of the control arm.

• "survTimeT": survival at the event times for the observations of the treatment arm.

• "survJumpC": survival at the jump times for the survival model in the control arm.

• "survJumpT": survival at the time times for the survival model in the treatment arm.

• "lastSurv": survival at the last event time.

Author(s)

Brice Ozenne

Extract the idd Decomposition for the AUC

Description

Extract the iid decompotion relative to AUC estimate.

Usage

## S3 method for class 'BuyseTestAuc'
iid(x, ...)

Arguments

Value

A column vector.

Extract the idd Decomposition for the Brier Score

Description

Extract the iid decompotion relative to Brier score estimate.

Usage

## S3 method for class 'BuyseTestBrier'
iid(x, ...)

Arguments

Value

A column vector.

Extract i.i.d. decomposition from a prodlim model

Description

Compute the influence function for each observation used to estimate the model

Usage

## S3 method for class 'prodlim'
iid(x, add0 = FALSE, ...)

Arguments

Details

This function is a simplified version of the iidCox function of the riskRegression package. Formula for the influence function can be found in (Ozenne et al., 2017).

Value

A list containing:

• IFbeta: Influence function for the regression coefficient.

• IFhazard: Time differential of the influence function of the hazard.

• IFcumhazard: Influence function of the cumulative hazard.

• time: Times at which the influence function has been evaluated.

• etime.max: Last observation time (i.e. jump or censoring) in each strata.

• label.strata: Strata to which each observation belong.

• X: Design matrix.

• table: Hazard at each time for each strata.

Author(s)

Brice Ozenne

References

Brice Ozenne, Anne Lyngholm Sorensen, Thomas Scheike, Christian Torp-Pedersen and Thomas Alexander Gerds. riskRegression: Predicting the Risk of an Event using Cox Regression Models. The R Journal (2017) 9:2, pages 440-460.

Examples

library(data.table)
library(prodlim)

set.seed(10)
dt <- simBuyseTest(10)
setkeyv(dt, "treatment")

e.KM <- prodlim(Hist(eventtime,status)~treatment, data = dt)
lava::iid(e.KM)

Inference using MOVER in match designs (EXPERIMENTAL)

Description

Implementation of the method of variance estimates recovery (MOVER), an alternative method for statistic inference in a matched design proposed by Matsouaka et al. (2022), for the Net Treatment Benefit.

Usage

mover(
  object,
  endpoint = NULL,
  conf.level = 0.95,
  p.value = TRUE,
  type = "Wilson",
  tol = 1e-06
)

Arguments

Details

The p-value calculation is performed by finding the confidence level such that the upper or lower bound of the corresponding confidence interval is 0. This was not part of the methodology presented in the original paper.

References

R A Matsouaka, A Coles (2022). Robust statistical inference for the matched net benefit and the matched win ratio using prioritized composite endpoints. Stat Methods Med Res 31(8):1423-1438. doi: 10.1177/09622802221090761.

Assess Performance of a Classifier

Description

Assess the performance in term of AUC and brier score of one or several binary classifiers. Currently limited to logistic regressions and random forest.

Usage

performance(
  object,
  data = NULL,
  newdata = NA,
  individual.fit = FALSE,
  impute = "none",
  name.response = NULL,
  fold.size = 1/10,
  fold.repetition = 0,
  fold.balance = FALSE,
  null = c(brier = NA, AUC = 0.5),
  conf.level = 0.95,
  se = TRUE,
  transformation = TRUE,
  auc.type = "classical",
  simplify = TRUE,
  trace = TRUE,
  seed = NULL
)

Arguments

Value

An S3 object of class performance.

References

LeDell E, Petersen M, van der Laan M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron J Stat. 2015;9(1):1583-1607. doi:10.1214/15-EJS1035

Examples

## Simulate data
set.seed(10)
n <- 100
df.train <- data.frame(Y = rbinom(n, prob = 0.5, size = 1), X1 = rnorm(n), X2 = rnorm(n))
df.test <- data.frame(Y = rbinom(n, prob = 0.5, size = 1), X1 = rnorm(n), X2 = rnorm(n))

## fit logistic model
e.null <- glm(Y~1, data = df.train, family = binomial(link="logit"))
e.logit1 <- glm(Y~X1, data = df.train, family = binomial(link="logit"))
e.logit2 <- glm(Y~X1+X2, data = df.train, family = binomial(link="logit"))

## assess performance on the training set (biased)
## and external dataset
performance(e.logit1, newdata = df.test)
e.perf <- performance(list(null = e.null, p1 = e.logit1, p2 = e.logit2),
                      newdata = df.test)
e.perf
summary(e.perf, order.model = c("null","p2","p1"))

## assess performance using cross validation
## Not run: 
set.seed(10)
performance(e.logit1, fold.repetition = 10, se = FALSE)
set.seed(10)
performance(list(null = e.null, prop = e.logit1), fold.repetition = 10)
performance(e.logit1, fold.repetition = c(50,20,10))

## End(Not run)

Uncertainty About Performance of a Classifier (EXPERIMENTAL)

Description

Use resampling to quantify uncertainties about the performance of one or several binary classifiers evaluated via cross-validation.

Usage

performanceResample(
  object,
  data = NULL,
  name.response = NULL,
  type.resampling = "permutation",
  n.resampling = 1000,
  fold.repetition = 0,
  conf.level = 0.95,
  cpus = 1,
  seed = NULL,
  trace = TRUE,
  filename = NULL,
  ...
)

Arguments

Details

WARNING: using bootstrap after cross-validation may not provide valid variance/CI/p-value estimates.

Value

An S3 object of class performance.

Graphical Display for Sensitivity Analysis

Description

Display the statistic of interest across various threshold values, possibly with confidence intervals. Currently only works when varying thresholds relative to one or two variables.

Usage

## S3 method for class 'S3sensitivity'
plot(x, plot = TRUE, ...)

## S3 method for class 'S3sensitivity'
autoplot(
  object,
  col = NULL,
  ci = TRUE,
  band = TRUE,
  label = "Threshold for",
  position = NULL,
  size.line = 1,
  size.point = 1.75,
  size.ci = 0.5,
  alpha = 0.1,
  ...
)

Arguments

Details

The autoplot and plot methods are very similar. The main difference is that the former returns a ggplot2 object whereas the later automatically display the figure in a graphical window and returns an (invible) list with the plot and the data.

Value

a ggplot2 object

Performing simulation studies with BuyseTest

Description

Performs a simulation studies for several sample sizes. Returns estimates, their standard deviation, the average estimated standard error, and the rejection rate. Can also be use for power calculation or to approximate the sample size needed to reach a specific power.

Usage

powerBuyseTest(
  formula,
  sim,
  sample.size,
  n.rep = c(1000, 10),
  null = c(netBenefit = 0),
  cpus = 1,
  export.cpus = NULL,
  seed = NULL,
  conf.level = NULL,
  power = NULL,
  max.sample.size = 2000,
  alternative = NULL,
  order.Hprojection = NULL,
  transformation = NULL,
  trace = 1,
  ...
)

Arguments

Details

Sample size calculation: to approximate the sample size achieving the requested type 1 (\alpha) and type 2 error (\beta), GPC are applied on a large sample (as defined by the argument max.sample.size): N^*=m^*+n^* where m^* is the sample size in the control group and n^* is the sample size in the active group. Then the effect (\delta) and the asymptotic variance of the estimator (\sigma^2) are estimated. The total sample size is then deduced as (two-sided case):

\hat{N} = \hat{\sigma}^2\frac{(u_{1-\alpha/2}+u_{1-\beta})^2}{\hat{\delta}^2}

from which the group specific sample sizes are deduced: \hat{m}=\hat{N}\frac{m^*}{N^*} and \hat{n}=\hat{N}\frac{n^*}{N^*}. Here u_x denotes the x-quantile of the normal distribution.
This approximation can be improved by increasing the sample size (argument max.sample.size) and/or by performing it multiple times based on a different dataset and average estimated sample size per group (second element of argument n.rep).
To evaluate the approximation, a simulation study is then performed with the estimated sample size. It will not exactly match the requested power but should provide a reasonnable guess which can be refined with further simulation studies. The larger the sample size (and/or number of CPUs) the more accurate the approximation.

seed: the seed is used to generate one seed per simulation. These simulation seeds are the same whether one or several CPUs are used.

Value

An S4 object of class S4BuysePower.

Author(s)

Brice Ozenne

Examples

library(data.table)

#### Using simBuyseTest ####
## save time by not generating TTE outcomes
simBuyseTest2 <- function(...){simBuyseTest(..., argsCont = NULL, argsTTE = NULL)}

## only point estimate
## Not run: 
pBT <- powerBuyseTest(sim = simBuyseTest2, sample.size = c(10, 25, 50, 75, 100), 
                  formula = treatment ~ bin(toxicity), seed = 10, n.rep = 1000,
                  method.inference = "none", keep.pairScore = FALSE, cpus = 5)
summary(pBT)
model.tables(pBT)

## End(Not run)

## point estimate with rejection rate

## Not run: 
powerBuyseTest(sim = simBuyseTest2, sample.size = c(10, 50, 100), 
               formula = treatment ~ bin(toxicity), seed = 10, n.rep = 1000,
               method.inference = "u-statistic", trace = 4)

## End(Not run)

#### Using user defined simulation function ####
## power calculation for Wilcoxon test
simFCT <- function(n.C, n.T){
    out <- rbind(cbind(Y=stats::rt(n.C, df = 5), group=0),
                 cbind(Y=stats::rt(n.T, df = 5), group=1) + 1)
    return(data.table::as.data.table(out))
}
simFCT2 <- function(n.C, n.T){
    out <- rbind(cbind(Y=stats::rt(n.C, df = 5), group=0),
                 cbind(Y=stats::rt(n.T, df = 5), group=1) + 0.25)
    return(data.table::as.data.table(out))
}


## Not run: 
powerW <- powerBuyseTest(sim = simFCT, sample.size = c(5,10,20,30,50,100),
                         n.rep = 1000, formula = group ~ cont(Y), cpus = "all")
summary(powerW)

## End(Not run) 

## sample size needed to reach (approximately) a power
## based on summary statistics obtained on a large sample 
## Not run: 
sampleW <- powerBuyseTest(sim = simFCT, power = 0.8, formula = group ~ cont(Y), 
                         n.rep = c(1000,10), max.sample.size = 2000, cpus = 5,
                         seed = 10)
nobs(sampleW)
summary(sampleW) ## not very accurate but gives an order of magnitude

sampleW2 <- powerBuyseTest(sim = simFCT2, power = 0.8, formula = group ~ cont(Y), 
                         n.rep = c(1000,10), max.sample.size = 2000, cpus = 5,
                         seed = 10)
summary(sampleW2) ## more accurate when the sample size needed is not too small

## End(Not run)

Prediction with Time to Event Model

Description

Evaluate the cumulative incidence function (cif) / survival in one of the treatment groups.

Usage

## S3 method for class 'BuyseTTEM'
predict(object, time, treatment, strata, cause = 1, iid = FALSE, ...)

Arguments

Value

a list containing the survival (element survival) or the cumulative incidence function (element cif), and possible standard errors (element .se) and influence function (element .iid).

RCT In Metastatic Pancreatic Cancer Comparing Two Chemoterapy.

Description

Simulated data inspired from the PRODIGE trial comparing the survival of patients with metastatic pancreatic cancer treated with FOLFIRINOX or gemcitabine .

• id: study participant.

• treatment: treatment arm: FOLFIRINOX (T) or gemcitabine (C).

• OS: time from inclusion (say diagnosis) to end of follow-up.

• statusOS: event triggering the end of follow-up: death (1), drop-out (0).

• PFS: time from inclusion (say diagnosis) to progression of the disease or end of follow-up.

• statusPFS: progression (1) or end of follow-up (0).

• toxicity: most serious side effect observed during the follow-up (1 mild, 6 severe).

• sex: male (M) or female (F)

Usage

data(prodige, package = "BuyseTest")

Format

An object of class data.table (inherits from data.frame) with 823 rows and 8 columns.

Author(s)

Brice Ozenne

References

Conroy, Thierry, et al. "FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer" New England Journal of Medicine (2011) 364(19):1817-25. doi: 10.1056/NEJMoa1011923.

Combine Resampling Results For Performance Objects

Description

Combine permutation or bootstrap samples. Useful to run parallel calculations (see example below).

Usage

## S3 method for class 'performance'
rbind(..., tolerance = 1e-05)

Arguments

Examples

if(FALSE){

#### simulate data ####
set.seed(10)
n <- 100
df.train <- data.frame(Y = rbinom(n, prob = 0.5, size = 1),
                       X1 = rnorm(n), X2 = rnorm(n), X3 = rnorm(n), X4 = rnorm(n),
                       X5 = rnorm(n), X6 = rnorm(n), X7 = rnorm(n), X8 = rnorm(n),
                       X9 = rnorm(n), X10 = rnorm(n))
df.train$Y <- rbinom(n, size = 1,
                     prob = 1/(1+exp(-df.train$X5 - df.train$X6 - df.train$X7)))

#### fit models ####
e.null <- glm(Y~1, data = df.train, family = binomial(link="logit"))
e.logit <- glm(Y~X1+X2, data = df.train, family = binomial(link="logit"))
e.logit2 <- glm(Y~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10, data = df.train,
               family = binomial(link="logit"))

#### evaluate model (same seed) ####
fold.repetition <- 5 ## 0: internal perf (fast)
                     ## >0: 10 fold CV repeated (slow)
test <- performanceResample(list(e.logit,e.logit2), seed = 10,
                             fold.repetition = fold.repetition, n.resampling = 100)
test.1 <- performanceResample(list(e.logit,e.logit2), seed = 10,
                             fold.repetition = fold.repetition, n.resampling = 1:50)
test.2 <- performanceResample(list(e.logit,e.logit2), seed = 10,
                             fold.repetition = fold.repetition, n.resampling = 51:100)
rbind(test.1,test.2)
test

## Note: when the prediction model call RNG then test.1 and test.2 may not give test 

#### evaluate model (different seed) ####
test.3 <- performanceResample(list(e.logit,e.logit2), seed = 11,
                             fold.repetition = fold.repetition, n.resampling = 1:50)
test.4 <- performanceResample(list(e.logit,e.logit2), seed = 12,
                             fold.repetition = fold.repetition, n.resampling = 51:100)
rbind(test.3,test.4, tolerance = NA) ## does not check equality of the point estimate
                                     ## between test.3 and test.4
test
}

Sensitivity Analysis for the Choice of the Thresholds

Description

Evaluate a summary statistic (net benefit, win ratio, ...) using GPC along various thresholds of clinical relevance.

Usage

## S4 method for signature 'S4BuyseTest'
sensitivity(
  object,
  threshold,
  statistic = NULL,
  band = FALSE,
  conf.level = NULL,
  null = NULL,
  transformation = NULL,
  alternative = NULL,
  adj.p.value = FALSE,
  trace = TRUE,
  cpus = 1,
  ...
)

Arguments

Details

Simulateneous confidence intervals and adjusted p-values are computed using a single-step max-test approach via the function transformCIBP of the riskRegression package.

Value

An S3 object of class S3sensitivity.

Examples

## Not run: 
require(ggplot2)

## simulate data
set.seed(10)
df.data <- simBuyseTest(1e2, n.strata = 2)

## with one endpoint
ff1 <- treatment ~ TTE(eventtime, status = status, threshold = 0.1)
BT1 <- BuyseTest(ff1, data= df.data)
se.BT1 <- sensitivity(BT1, threshold = seq(0,2,0.25), band = TRUE)
plot(se.BT1)

## with two endpoints
ff2 <- update(ff1, .~. + cont(score, threshold = 1))
BT2 <- BuyseTest(ff2, data= df.data)
se.BT2 <- sensitivity(BT2, threshold = list(eventtime = seq(0,2,0.25), score = 0:2),
                      band = TRUE)
plot(se.BT2)
plot(se.BT2, col = NA)

## End(Not run)

Simulation of data for the BuyseTest

Description

Simulate categorical, continuous or time to event endpoints, possibly along with a strata variable. Categorical endpoints are simulated by thresholding a latent Gaussian variable (tobit model), continuous endpoints are simulated using a Gaussian distribution, and time to event endpoints are simulated using Weibull distributions for the event of interest, competing events, and censoring. This function is built upon the lvm and sim functions from the lava package.

Usage

simBuyseTest(
  n.T,
  n.C = NULL,
  argsBin = list(),
  argsCont = list(),
  argsTTE = list(),
  names.strata = NULL,
  level.strata = NULL,
  n.strata = NULL,
  name.cluster = "id",
  prefix.cluster = NULL,
  name.treatment = "treatment",
  level.treatment = c("C", "T"),
  format = "data.table",
  latent = FALSE
)

Arguments

Details

Endpoints are simulated independently of the strata variable and independently of each other, with the exception of categorical endpoint and the time to event endpoints that can be correlated by specifying a non-0 value for the rho.T and rho.C elements of the argument argsBin.

Arguments in the list argsBin:

• p.T list of probabilities for the values taken by each endpoint (categorical endpoint, treatment group).

• p.C same as p.T but for the control group.

• rho.T value of the regression coefficient between the underlying latent variable and the survival time. Only implemented for weibull distributed survival times.

• rho.C same as rho.T but for the control group.

• name names of the binary variables.

Arguments in the list argsCont:

• mu.T expected value of each endpoint (continuous endpoint, treatment group).

• mu.C same as mu.C but for the control group.

• sigma.T standard deviation of the values of each endpoint (continuous endpoint, treatment group).

• sigma.C same as sigma.T but for the control group.

• name names of the continuous variables.

Arguments in the list argsTTE:

• CR should competing risks be simulated?

• scale.T,scale.C,scale.CR,scale.censoring.T,scale.censoring.C scale parameter of the Weibull distribution for, respectively, the event of interest in the treatment group, the event of interest in the control group, the competing event in both groups, the censoring mechanism in the treatment group, the censoring mechanism in the control group

• shape.T,shape.C,shape.CR,shape.censoring.T,shape.censoring.C shape parameter of the Weibull distribution for, respectively, the event of interest in the treatment group, the event of interest in the control group, the competing event in both groups, the censoring mechanism in the treatment group, the censoring mechanism in the control group

• dist.T,dist.C,dist.CR,dist.censoring.T,dist.censoring.C type of distribution ("weibull", "uniform", "piecewiseExp") for, respectively, the event of interest in the treatment group, the event of interest in the control group, the competing event in both groups, the censoring mechanism in the treatment group, the censoring mechanism in the control group. For uniform distirbutions the (scale,shape) parameters becomes the support (min, max) of the censoring distribution. For piecewise exponential distributions the (scale,shape) should be lists of numeric (see example) and the shape parameters becomes the time parameters (first element should be 0).

• name names of the time to event variables.

• name.censoring names of the event type indicators. #'

Value

Depends on the argument format: either a data.frame, data.table, matrix containing the simulated data, or a function that can be used to simulate data.

Author(s)

Brice Ozenne

Examples

library(data.table)

n <- 1e2

#### by default ####
simBuyseTest(n)

## with a strata variable having 5 levels
simBuyseTest(n, n.strata = 5)
## with a strata variable named grade
simBuyseTest(n, n.strata = 5, names.strata = "grade")
## several strata variables
simBuyseTest(1e3, n.strata = c(2,4), names.strata = c("Gender","AgeCategory"))

#### only categorical endpoints ####
args <- list(p.T = list(c(low=0.1,moderate=0.5,high=0.4)))
dt.bin <- simBuyseTest(n, argsBin = args, argsCont = NULL, argsTTE = NULL)
table(dt.bin$toxicity)/NROW(dt.bin)

args <- list(p.T = list(c(low=0.1,moderate=0.5,high=0.4), c(0.1,0.9)))
dt.bin <- simBuyseTest(n, argsBin = args, argsCont = NULL, argsTTE = NULL)
table(dt.bin$toxicity1)/NROW(dt.bin)
table(dt.bin$toxicity2)/NROW(dt.bin)

#### only continuous endpoints ####
args <- list(mu.T = c(3:5/10), sigma.T = rep(1,3))
dt.cont <- simBuyseTest(n, argsBin = NULL, argsCont = args, argsTTE = NULL)
c(mean(dt.cont$score1), mean(dt.cont$score2), mean(dt.cont$score3))
c(sd(dt.cont$score1), sd(dt.cont$score2), sd(dt.cont$score3))

#### only TTE endpoints ####
## weibull distributed
args <- list(scale.T = c(3:5/10), scale.censoring.T = rep(1,3))
dt.tte <- simBuyseTest(n, argsBin = NULL, argsCont = NULL, argsTTE = args)
1/c(sum(dt.tte$eventtime1)/sum(dt.tte$status1),
  sum(dt.tte$eventtime2)/sum(dt.tte$status2),
  sum(dt.tte$eventtime3)/sum(dt.tte$status3))
        
1/c(sum(dt.tte$eventtime1)/sum(dt.tte$status1==0),
  sum(dt.tte$eventtime2)/sum(dt.tte$status2==0),
  sum(dt.tte$eventtime3)/sum(dt.tte$status3==0))

hist(dt.tte$eventtime1)

## uniform distributed
args <- list(scale.T = 0, shape.T = 1, dist.T = "uniform", scale.censoring.T = 1e5,
             scale.C = 0, shape.C = 2, dist.C = "uniform", scale.censoring.C = 1e5)
dt.tte <- simBuyseTest(n, argsBin = NULL, argsCont = NULL, argsTTE = args)

par(mfrow=c(1,2))
hist(dt.tte$eventtime[dt.tte$treatment=="C"])
hist(dt.tte$eventtime[dt.tte$treatment=="T"])

## piecewise constant exponential distributed
## time [0;4]: scale parameter 10
## time [4;12]: scale parameter 13
## time [12;18.]: scale parameter 18
## time [18.5;36]: scale parameter 31
## after that: scale parameter 37
vec.scale <- list(c(10,13,18,31,100))
vec.time <- list(c(0,4,12,18.5,36))
args <- list(scale.T = vec.scale, shape.T = vec.time, dist.T = "piecewiseExp",
             scale.C = 10, shape.C = 1, dist.C = "weibull",
             scale.censoring.T = 1e5)
dt.tte <- simBuyseTest(n, argsBin = NULL, argsCont = NULL, argsTTE = args)

if(require(prodlim)){
plot(prodlim(Hist(eventtime,status)~treatment, data = dt.tte))
}

#### correlated categorical / time to event endpoint ####
## WARNING: only for weibull distributed time to event endpoint
args.bin <- list(p.T = list(c(low=0.1,moderate=0.5,high=0.4)), rho.T = 1)
args.tte <- list(scale.T = 2, scale.censoring.T = 1)
dt.corr <- simBuyseTest(n, argsBin = args.bin, argsCont = NULL, argsTTE = args.tte)

1/(sum(dt.corr$eventtime)/sum(dt.corr$status))
1/(sum(dt.corr$eventtime)/sum(dt.corr$status==0))
table(dt.corr$toxicity)/NROW(dt.corr)

boxplot(eventtime ~ toxicity, data = dt.corr)

#### pre-computation ####
library(lava)
mySimulator <- simBuyseTest(n, format = "function") ## creates lvm object once for all
set.seed(1)
sim(mySimulator)
set.seed(2)
sim(mySimulator) 

Simulation of Gompertz competing risks data for the BuyseTest

Description

Simulate Gompertz competing risks data with proportional (via prespecified sub-distribution hazard ratio) or non-proportional sub-distribution hazards. A treatment variable with two groups (treatment and control) is created.

Usage

simCompetingRisks(
  n.T,
  n.C,
  p.1C = NULL,
  v.1C,
  v.1T,
  v.2C,
  v.2T,
  sHR = NULL,
  b.1T = NULL,
  b.1C = NULL,
  b.2T = NULL,
  b.2C = NULL,
  cens.distrib = NULL,
  param.cens = NULL,
  latent = NULL
)

Arguments

Details

The times to the event of interest and to the competing event in each group follow an improper Gompertz distribution (see Jeong and Fine, 2006), whose cumulative distribution function is

F(t; b, v) = 1 - exp(b (1 - exp (v t)) / v)

and hazard functions is

h(t; b, v) = b exp(v t)

The shape parameters must be negative to have improper distributions for the times to the two events in each group. Note however that in each group, the overall cumulative incidence function must be proper (i.e. the maximum values of the cumulative incidence of each event type sum up to 1 in each group). When only providing the shape parameters, the rate parameters are computed to fulfill this condition. In case you whish to provide the rate parameters too, make sure that the condition is met.

Value

A data.frame

Author(s)

Eva Cantagallo

References

Jeong J-H. and Fine J. (2006) Direct parametric inference for the cumulative incidence function. Journal of the Royal Statistical Society 55: 187-200

Examples

#### Providing p.1C and sHR ####
d <- simCompetingRisks(n.T = 100, n.C = 100, p.1C = 0.55, v.1C = -0.30, 
v.1T = -0.30, v.2C = -0.30, v.2T = -0.30, sHR = 0.5, b.1T = NULL, 
b.1C = NULL, b.2T = NULL, b.2C = NULL)

#### Providing the rate parameters ####
d <- simCompetingRisks(n.T = 100, n.C = 100, p.1C = NULL, v.1C = -0.30, 
v.1T = -0.30, v.2C = -0.30, v.2T = -0.30, sHR = NULL, b.1T = 0.12, 
b.1C = 0.24, b.2T = 0.33, b.2C = 0.18)

#### With exponential censoring ####
d <- simCompetingRisks(n.T = 100, n.C = 100, p.1C = 0.55, v.1C = -0.30, 
v.1T = -0.30, v.2C = -0.30, v.2T = -0.30, sHR = 0.5, b.1T = NULL, 
b.1C = NULL, b.2T = NULL, b.2C = NULL, cens.distrib = "exponential", 
param.cens = 0.8, latent = TRUE)

### With uniform censoring ####
d <- simCompetingRisks(n.T = 100, n.C = 100, p.1C = 0.55, v.1C = -0.30, 
v.1T = -0.30, v.2C = -0.30, v.2T = -0.30, sHR = 0.5, b.1T = NULL, 
b.1C = NULL, b.2T = NULL, b.2C = NULL, cens.distrib = "uniform", 
param.cens = c(0, 7), latent=TRUE)        

Summary Method for Performance Objects

Description

Summary of the performance of binary classifiers

Usage

## S3 method for class 'performance'
summary(object, order.model = NULL, digits = c(3, 3), print = TRUE, ...)

Arguments

Check Arguments of a function.

Description

Check the validity of the arguments in functions.

Usage

validCharacter(
  value1,
  name1 = as.character(substitute(value1)),
  valid.length,
  valid.values = "character",
  refuse.NULL = TRUE,
  refuse.duplicates = FALSE,
  method = NULL,
  addPP = TRUE
)

validClass(
  value1,
  name1 = as.character(substitute(value1)),
  valid.class,
  type = "inherits",
  method = NULL,
  addPP = TRUE
)

validDimension(
  value1,
  value2 = NULL,
  name1 = as.character(substitute(value1)),
  name2 = as.character(substitute(value2)),
  valid.dimension = NULL,
  type = c("NROW", "NCOL"),
  method = NULL,
  addPP = TRUE
)

validInteger(
  value1,
  name1 = as.character(substitute(value1)),
  valid.length,
  min = NULL,
  max = NULL,
  refuse.NA = TRUE,
  refuse.NULL = TRUE,
  refuse.duplicates = FALSE,
  method = NULL,
  addPP = TRUE
)

validLogical(
  value1,
  name1 = as.character(substitute(value1)),
  valid.length,
  refuse.NULL = TRUE,
  refuse.NA = TRUE,
  method = NULL,
  addPP = TRUE
)

validNames(
  value1,
  name1 = as.character(substitute(value1)),
  refuse.NULL = TRUE,
  valid.length = NULL,
  valid.values = NULL,
  required.values = NULL,
  refuse.values = NULL,
  method = NULL,
  addPP = TRUE
)

validNumeric(
  value1,
  name1 = as.character(substitute(value1)),
  valid.length,
  valid.values = NULL,
  min = NULL,
  max = NULL,
  refuse.NA = TRUE,
  refuse.NULL = TRUE,
  refuse.duplicates = FALSE,
  method = NULL,
  addPP = TRUE,
  unlist = FALSE
)

validPath(
  value1,
  name1 = as.character(substitute(value1)),
  type,
  method = NULL,
  addPP = TRUE,
  extension = NULL,
  check.fsep = FALSE
)

Arguments

Value

An invisible TRUE or an error message.