computeLogrankBetaFrom: Helper function: Computes the type II error under optional...

In safestats: Safe Anytime-Valid Inference

Helper function: Computes the type II error under optional stopping based on the minimal clinically relevant hazard ratio and the maximum number of nEvents.

Description

Helper function: Computes the type II error under optional stopping based on the minimal clinically relevant hazard ratio and the maximum number of nEvents.

Usage

computeLogrankBetaFrom(
  hrMin,
  nEvents,
  alpha = 0.05,
  alternative = c("twoSided", "greater", "less"),
  m0 = 50000L,
  m1 = 50000L,
  testType = c("oneSample", "paired", "twoSample"),
  ratio = 1,
  parameter = NULL,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow"),
  wantSamplePaths = TRUE,
  groupSizePerTimeFunction = returnOne,
  pb = TRUE,
  seed = NULL,
  nSim = 1000L,
  nBoot = nSim,
  relevanceTest = FALSE,
  ...
)

Arguments

hrMin	numeric that defines the minimal relevant hazard ratio, the smallest hazard ratio that we want to detect.
nEvents	numeric > 0, targetted number of events.
alpha	numeric in (0, 1) that specifies the tolerable type I error and the null rejection rule e >= 1/alpha.
alternative	a character string specifying the alternative hypothesis, which must be one of "twoSided" (default),"greater" or "less". The alternative is pitted against the null hypothesis of equality of the survival distributions. More specifically, let lambda1 be the hazard rate of group 1 (i.e., placebo), and lambda2 the hazard ratio of group 2 (i.e., treatment), then the null hypothesis states that the hazard ratio theta = lambda2/lambda1 = 1. If alternative = "less", the null hypothesis is compared to theta < 1, thus, lambda2 < lambda1, that is, the hazard of group 2 (i.e., treatment) is less than that of group 1 (i.e., placebo), hence, the treatment is beneficial. If alternative = "greater", then the null hypothesis is compared to theta > 1, thus, lambda2 > lambda1, hence, harm.
m0	Number of subjects in the control group 0/1 at the beginning of the trial, i.e., nPlan[1].
m1	Number of subjects in the treatment group 1/2 at the beginning of the trial, i.e., nPlan[2].
testType	either one of "oneSample", "paired", "twoSample".
ratio	numeric > 0 representing the randomisation ratio of condition 2 (Treatment) over condition 1 (Placebo), thus, m1/m0. Note that m1 and m0 are not used to specify ratio. Ratio is only used when zApprox=TRUE, which ignores m1 and m0.
parameter	Numeric > 0, represents the savi tests defining thetaS. Default NULL so it's decided by the algorithm, typically, this equals hrMin, which corresponds to the GROW choice.
eType	character one of "mom", "grow", "eGauss", and "eCauchy". "mom" is default and uses a non-local moment prior with bump(s) at meanDiffMin, "grow" uses point prior(s) at meanDiffMin, "eGauss" a zero-centred normal prior, "eCauchy" a zero centred Cauchy prior.
wantSamplePaths	logical, if TRUE then also outputs the sample paths.
groupSizePerTimeFunction	A function without parameters and integer output. This function provides the number of events at each time step. For instance, if rpois(1, 7) leads to a random number of events at each time step.
pb	logical, if TRUE, then show progress bar.
seed	integer, seed number.
nSim	integer > 0, the number of simulations needed to compute power or the number of events for the exact savi logrank test under continuous monitoring
nBoot	integer > 0 representing the number of bootstrap samples to assess the accuracy of the approximation of power or nEvents for the exact savi logrank test under continuous monitoring
relevanceTest	logical, if TRUE then impose rule to stop for minimal efficiency if e <= alphaRelevance. Default FALSE.
...	further arguments to be passed to or from methods.

Value

a list which contains at least beta and an adapted bootObject of class boot.

Author(s)

Muriel Felipe Perez-Ortiz and Alexander Ly

References

Grünwald, P. D., de Heide, R., & Koolen, W. (2024). Safe testing. Journal of the Royal Statistical Society. Series B (Methodological), 86(5), 1091-1128. (With discussions), https://doi.org/10.1093/jrsssb/qkae011. ter Schure, J., Pérez-Ortiz, M. F., Ly, A., & Grünwald, P. D. (2024). The Safe Logrank Test: Error control under continuous monitoring with unlimited horizon. The New England Journal of Statistics in Data Science, 2(2), 190-214, https://doi.org/10.51387/24-NEJSDS65.

Examples

computeLogrankBetaFrom(hrMin=0.7, 300, nSim=10)

safestats documentation built on Sept. 6, 2026, 1:06 a.m.