rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

getSampleSizeMeans {rpact}R Documentation

Get Sample Size Means


Returns the sample size for testing means in one or two samples.


getSampleSizeMeans(design = NULL, ..., groups = 2,
  normalApproximation = FALSE, meanRatio = FALSE,
  thetaH0 = ifelse(meanRatio, 1, 0),
  allocationRatioPlanned = NA_real_)



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


Ensures that all arguments are 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.


If normalApproximation = TRUE is specified, the variance is assumed to be known, default is FALSE, i.e., the calculations are performed with the t distribution.


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


The null hypothesis value. For one-sided testing, a value != 0 (or a value != 1 for testing the mean ratio) can be specified, default is 0 or 1 for difference and ratio testing, respectively.


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


The standard deviation, default is 1. If meanRatio = TRUE is specified, stDev defines the coefficient of variation sigma/mu2.


The planned allocation ratio for a two treatment groups design, default is 1. If allocationRatioPlanned = 0 is entered, the optimal allocation ratio yielding the smallest overall sample size is determined.


At given design the function calculates the stage-wise (non-cumulated) and maximum sample size for testing means. 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. Critical bounds and stopping for futility bounds are provided at the effect scale (mean, mean difference, or mean ratio, respectively) for each sample size calculation separately.


Returns a TrialDesignPlanMeans object.


# Calculate sample sizes in a fixed sample size parallel group design 
# with allocation ratio n1/n2 = 2 for a range of alternative values 1,...,5 
# with assumed standard deviation = 3.5; two-sided alpha = 0.05, power 1 - beta = 90%:
getSampleSizeMeans(alpha = 0.05, beta = 0.1, sided = 2, groups = 2, 
    alternative = seq(1, 5, 1), stDev = 3.5, allocationRatioPlanned = 2)

# Calculate sample sizes in a three-stage Pocock paired comparison design testing 
# H0: mu = 2 for a range of alternative values 3,4,5 with assumed standard 
# deviation = 3.5; one-sided alpha = 0.05, power 1 - beta = 90%:
getSampleSizeMeans(getDesignGroupSequential(typeOfDesign = "P", alpha = 0.05, 
    sided = 1, beta = 0.1), groups = 1, thetaH0 = 2, 
    alternative = seq(3, 5, 1), stDev = 3.5)

[Package rpact version 2.0.5 Index | www.rpact.org]