getPowerMeans {rpact}R Documentation

Get Power Means


Returns the power, stopping probabilities, and expected sample size for testing means in one or two samples at given maximum sample size.


  design = NULL,
  groups = 2L,
  normalApproximation = FALSE,
  meanRatio = FALSE,
  thetaH0 = ifelse(meanRatio, 1, 0),
  alternative = seq(0, 1, 0.2),
  stDev = 1,
  directionUpper = NA,
  maxNumberOfSubjects = NA_real_,
  allocationRatioPlanned = NA_real_



The trial design. If no trial design is specified, a fixed sample size design is used. In this case, Type I error rate alpha, Type II error rate beta, twoSidedPower, and sided can be directly entered as argument where necessary.


Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.


The number of treatment groups (1 or 2), default is 2.


The type of computation of the p-values. If TRUE, the variance is assumed to be known, default is FALSE, i.e., the calculations are performed with the t distribution.


If TRUE, the sample size for one-sided testing of H0: mu1 / mu2 = thetaH0 is calculated, default is FALSE.


The null hypothesis value, default is 0 for the normal and the binary case (testing means and rates, respectively), it is 1 for the survival case (testing the hazard ratio).

For non-inferiority designs, thetaH0 is the non-inferiority bound. That is, in case of (one-sided) testing of

  • means: a value != 0 (or a value != 1 for testing the mean ratio) can be specified.

  • rates: a value != 0 (or a value != 1 for testing the risk ratio pi1 / pi2) can be specified.

  • survival data: a bound for testing H0: hazard ratio = thetaH0 != 1 can be specified.

  • count data: a bound for testing H0: lambda1 / lambda2 = thetaH0 != 1 can be specified.

For testing a rate in one sample, a value thetaH0 in (0, 1) has to be specified for defining the null hypothesis H0: pi = thetaH0.


The alternative hypothesis value for testing means. This can be a vector of assumed alternatives, default is seq(0, 1, 0.2) (power calculations) or seq(0.2, 1, 0.2) (sample size calculations).


The standard deviation under which the sample size or power calculation is performed, default is 1. If meanRatio = TRUE is specified, stDev defines the coefficient of variation sigma / mu2. Must be a positive numeric of length 1.


Logical. Specifies the direction of the alternative, only applicable for one-sided testing; default is TRUE which means that larger values of the test statistics yield smaller p-values.


maxNumberOfSubjects > 0 needs to be specified for power calculations or calculation of necessary follow-up (count data). For two treatment arms, it is the maximum number of subjects for both treatment arms.


The planned allocation ratio n1 / n2 for a two treatment groups design, default is 1. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control. For simulating means and rates for a two treatment groups design, it can be a vector of length kMax, the number of stages. It can be a vector of length kMax, too, for multi-arm and enrichment designs. In these cases, a change of allocating subjects to treatment groups over the stages can be assessed. Note that internally allocationRatioPlanned is treated as a vector of length kMax, not a scalar.


At given design the function calculates the power, stopping probabilities, and expected sample size for testing means at given sample size. In a two treatment groups design, additionally, an allocation ratio = n1 / n2 can be specified where n1 and n2 are the number of subjects in the two treatment groups. A null hypothesis value thetaH0 != 0 for testing the difference of two means or thetaH0 != 1 for testing the ratio of two means can be specified. For the specified sample size, critical bounds and stopping for futility bounds are provided at the effect scale (mean, mean difference, or mean ratio, respectively)


Returns a TrialDesignPlan object. The following generics (R generic functions) are available for this result object:

How to get help for generic functions

Click on the link of a generic in the list above to go directly to the help documentation of the rpact specific implementation of the generic. Note that you can use the R function methods to get all the methods of a generic and to identify the object specific name of it, e.g., use methods("plot") to get all the methods for the plot generic. There you can find, e.g., plot.AnalysisResults and obtain the specific help documentation linked above by typing ?plot.AnalysisResults.

See Also

Other power functions: getPowerCounts(), getPowerRates(), getPowerSurvival()


# Calculate the power, stopping probabilities, and expected sample size 
# for testing H0: mu1 - mu2 = 0 in a two-armed design against a range of 
# alternatives H1: mu1 - m2 = delta, delta = (0, 1, 2, 3, 4, 5), 
# standard deviation sigma = 8, maximum sample size N = 80 (both treatment 
# arms), and an allocation ratio n1/n2 = 2. The design is a three stage 
# O'Brien & Fleming design with non-binding futility bounds (-0.5, 0.5) 
# for the two interims. The computation takes into account that the t test 
# is used (normalApproximation = FALSE). 
getPowerMeans(getDesignGroupSequential(alpha = 0.025, 
    sided = 1, futilityBounds = c(-0.5, 0.5)), 
    groups = 2, alternative = c(0:5), stDev = 8,
    normalApproximation = FALSE, maxNumberOfSubjects = 80, 
    allocationRatioPlanned = 2)

[Package rpact version 4.0.0 Index]