rpact (version 2.0.6)

getSampleSizeRates: Get Sample Size Rates

Description

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

Usage

getSampleSizeRates(
  design = NULL,
  ...,
  groups = 2,
  normalApproximation = TRUE,
  riskRatio = FALSE,
  thetaH0 = ifelse(riskRatio, 1, 0),
  pi1 = seq(0.4, 0.6, 0.1),
  pi2 = 0.2,
  allocationRatioPlanned = NA_real_
)

Arguments

design

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.

groups

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

normalApproximation

If normalApproximation = FALSE is specified, the sample size for the case of one treatment group is calculated exactly using the binomial distribution, default is TRUE.

riskRatio

If riskRatio = TRUE is specified, the sample size for one-sided testing of H0: pi1/pi2 = thetaH0 is calculated, default is FALSE.

thetaH0

The null hypothesis value. For one-sided testing, a value != 0 (or != 1 for testing the risk ratio pi1/pi2) can be specified, default is 0 or 1 for difference and ratio testing, respectively.

pi1

The assumed probability in the active treatment group if two treatment groups are considered, or the alternative probability for a one treatment group design, default is seq(0.4,0.6,0.1).

pi2

The assumed probability in the reference group if two treatment groups are considered, default is 0.2.

allocationRatioPlanned

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

Value

Returns a TrialDesignPlanRates object.

Details

At given design the function calculates the stage-wise (non-cumulated) and maximum sample size for testing rates. In a two treatment groups design, additionally, an allocation ratio = n1/n2 can be specified. If a null hypothesis value thetaH0 != 0 for testing the difference of two rates thetaH0 != 1 for testing the risk ratio is specified, the sample size formula according to Farrington & Manning (Statistics in Medicine, 1990) is used. Critical bounds and stopping for futility bounds are provided at the effect scale (rate, rate difference, or rate ratio, respectively) for each sample size calculation separately. For the two-sample case, the calculation here is performed at fixed pi2 as given as argument in the function.

Examples

# NOT RUN {
# Calculate the stage-wise sample sizes, maximum sample sizes, and the optimum 
# allocation ratios for a range of pi1 values when testing 
# H0: pi1 - pi2 = -0.1 within a two-stage O'Brien & Fleming design;
# alpha = 0.05 one-sided, power 1- beta = 90%:
getSampleSizeRates(design = getDesignGroupSequential(kMax = 2, alpha = 0.05, beta = 0.1, 
    sided = 1), groups = 2, thetaH0 = -0.1, pi1 = seq(0.4, 0.55, 0.025), 
    pi2 = 0.4, allocationRatioPlanned = 0)

# Calculate the stage-wise sample sizes, maximum sample sizes, and the optimum 
# allocation ratios for a range of pi1 values when testing 
# H0: pi1 / pi2 = 0.80 within a three-stage O'Brien & Fleming design;
# alpha = 0.025 one-sided, power 1- beta = 90%:
getSampleSizeRates(getDesignGroupSequential(kMax = 3, alpha = 0.025, beta = 0.1, 
    sided = 1), groups = 2, riskRatio = TRUE, thetaH0 = 0.80, pi1 = seq(0.3,0.5,0.025), 
    pi2 = 0.3, allocationRatioPlanned = 0)

# }