rpact: Confirmatory Adaptive Clinical Trial Design and Analysis


getPowerMeans {rpact}R Documentation

Get Power Means

Description

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

Usage

getPowerMeans(
  design = NULL,
  ...,
  groups = 2,
  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_
)

Arguments

design

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.

groups

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

normalApproximation

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.

meanRatio

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

thetaH0

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.

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.

alternative

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

stDev

The standard deviation under which the conditional power calculation is performed, default is 1. If meanRatio = TRUE is specified, stDev defines the coefficient of variation sigma / mu2.

directionUpper

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

maxNumberOfSubjects > 0 needs to be specified. For two treatment arms, it is the maximum number of subjects for both treatment arms.

allocationRatioPlanned

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.

Details

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. 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)

Value

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: getPowerRates(), getPowerSurvival()

Examples

# 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 3.0.0 Index | www.rpact.org]