getSampleSizeMeans {rpact}

Get Sample Size Means

Description

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

Usage

getSampleSizeMeans(
  design = NULL,
  ...,
  groups = 2L,
  normalApproximation = FALSE,
  meanRatio = FALSE,
  thetaH0 = ifelse(meanRatio, 1, 0),
  alternative = seq(0.2, 1, 0.2),
  stDev = 1,
  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.

• 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.

alternative

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).

stDev

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.

allocationRatioPlanned

The planned allocation ratio n1 / n2 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.

Details

At given design the function calculates the stage-wise and maximum sample size for testing means. 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. 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.

Value

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

• names() to obtain the field names,

• print() to print the object,

• summary() to display a summary of the object,

• plot() to plot the object,

• as.data.frame() to coerce the object to a data.frame,

• as.matrix() to coerce the object to a matrix.

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 sample size functions: getSampleSizeCounts(), getSampleSizeRates(), getSampleSizeSurvival()

Examples

# Calculate sample sizes in a fixed sample size parallel group design 
# with allocation ratio \code{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)
## Not run: 
# 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)

## End(Not run)