fseqbon: Group Sequential Trials Using Bonferroni-Based Graphical...

In lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Group Sequential Trials Using Bonferroni-Based Graphical Approaches

Description

Obtains the test results for group sequential trials using graphical approaches based on weighted Bonferroni tests.

Usage

fseqbon(
  w,
  G,
  alpha = 0.025,
  kMax,
  typeAlphaSpending = NULL,
  parameterAlphaSpending = NULL,
  maxInformation = NULL,
  incidenceMatrix = NULL,
  k1,
  p,
  information,
  spendingTime = NULL,
  lookback = TRUE,
  nthreads = 0
)

Arguments

w	The vector of initial weights for elementary hypotheses.
G	The initial transition matrix.
alpha	The significance level. Defaults to 0.025.
kMax	The maximum number of stages.
typeAlphaSpending	The vector of alpha spending functions for the hypotheses. Each element is one of the following: "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function. Defaults to "sfOF" if not provided.
parameterAlphaSpending	The vector of parameter values for the alpha spending functions for the hypotheses. Each element corresponds to the value of \rho for "sfKD" or \gamma for "sfHSD". Defaults to missing if not provided.
maxInformation	The vector of target maximum information for each hypothesis. Defaults to a vector of 1s if not provided.
incidenceMatrix	The kMax x m incidence matrix indicating whether the specific hypothesis will be tested at the given look. Here m is the number of hypotheses. If not provided, defaults to testing each hypothesis at all study looks.
k1	The number of study looks at the interim analysis.
p	The B k_1 \times m matrix of raw p-values for each hypothesis by study look. Here B is the number of replications for simulations. In other words, the number of rows must be a multiple of k1.
information	The B k_1 \times m matrix of observed information for each hypothesis by study look.
spendingTime	The B k_1 \times m matrix of spending time for alpha spending by study look. Each element must be between 0 and 1, and the spending time must be increasing by study look for each hypothesis. If not provided, it is the same as informationRates calculated from information and maxInformation.
lookback	Whether to allow retesting at earlier looks. It defaults to TRUE.
nthreads	The number of threads to use in simulations (0 means the default RcppParallel behavior).

Details

When lookback = TRUE, the procedure allows the user to retest an unrejected hypothesis at earlier looks if its weight increased after rejection of other hypotheses. The procedure will return the first look at which the specific hypothesis is rejected. If the hypothesis is not rejected at any look, it will return 0.

Value

A vector to indicate the first look the specific hypothesis is rejected (0 if the hypothesis is not rejected).

Author(s)

Kaifeng Lu, [e-mail]

References

Willi Maurer and Frank Bretz. Multiple testing in group sequential trials using graphical approaches. Statistics in Biopharmaceutical Research. 2013; 5:311-320.

Examples

# Case study from Maurer & Bretz (2013)

fseqbon(
  w = c(0.5, 0.5, 0, 0),
  G = matrix(c(0, 0.5, 0.5, 0,  0.5, 0, 0, 0.5,
               0, 1, 0, 0,  1, 0, 0, 0),
             nrow=4, ncol=4, byrow=TRUE),
  alpha = 0.025,
  kMax = 3,
  typeAlphaSpending = rep("sfOF", 4),
  maxInformation = rep(1, 4),
  k1 = 2,
  p = matrix(c(0.0062, 0.017, 0.009, 0.13,
               0.0002, 0.0035, 0.002, 0.06),
             nrow=2, ncol=4, byrow=TRUE),
  information = matrix(c(rep(1/3, 4), rep(2/3, 4)),
                       nrow=2, ncol=4, byrow=TRUE),
  nthreads = 1)

lrstat documentation built on Aug. 25, 2026, 5:07 p.m.