rpact (version 2.0.6)

getPowerRates: Get Power Rates

Description

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

Usage

getPowerRates(
  design = NULL,
  ...,
  groups = 2,
  riskRatio = FALSE,
  thetaH0 = ifelse(riskRatio, 1, 0),
  pi1 = C_PI_1_DEFAULT,
  pi2 = 0.2,
  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, alpha, beta, 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.

riskRatio

If riskRatio = TRUE is specified, the power 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(0.2,0.5,0.1).

pi2

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

directionUpper

Specifies the direction of the alternative, only applicable for one-sided testing, default is TRUE.

maxNumberOfSubjects

maxNumberOfSubjects > 0 needs to be specified.

allocationRatioPlanned

The planned allocation ratio for a two treatment groups design, default is 1.

Value

Returns a TrialDesignPlanRates object.

Details

At given design the function calculates the power, stopping probabilities, and expected sample size, for testing rates for given maximum sample size. The sample sizes over the stages are calculated according to the specified information rate in the design. 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 or thetaH0 != 1 for testing the risk ratio is specified, the formulas according to Farrington & Manning (Statistics in Medicine, 1990) are used (only one-sided testing). Critical bounds and stopping for futility bounds are provided at the effect scale (rate, rate difference, or rate ratio, respectively). For the two-sample case, the calculation here is performed at fixed pi2 as given as argument in the function. Note that the power calculation for rates is always based on the normal approximation.

Examples

# NOT RUN {
# Calculate the power, stopping probabilities, and expected sample size in a two-armed 
# design at given maximum sample size N = 200 
# in a three-stage O'Brien & Fleming design with information rate vector (0.2,0.5,1), 
# non-binding futility boundaries (0,0), i.e.,
# the study stops for futility if the p-value exceeds 0.5 at interm, and 
# allocation ratio = 2 for a range of pi1 values when testing H0: pi1 - pi2 = -0.1:
getPowerRates(getDesignGroupSequential(informationRates = c(0.2,0.5,1), 
    futilityBounds = c(0,0)), groups = 2, thetaH0 = -0.1, 
    pi1 = seq(0.3, 0.6, 0.1), directionUpper = FALSE, 
    pi2 = 0.7, allocationRatioPlanned = 2, maxNumberOfSubjects = 200)

# Calculate the power, stopping probabilities, and expected sample size in a single 
# arm design at given maximum sample size N = 60 in a three-stage two-sided 
# O'Brien & Fleming design with information rate vector (0.2,0.5,1) 
# for a range of pi1 values when testing H0: pi = 0.3:
getPowerRates(getDesignGroupSequential(informationRates = c(0.2,0.5,1), 
    sided = 2), groups = 1, thetaH0 = 0.3, pi1 = seq(0.3, 0.5, 0.05),  
    maxNumberOfSubjects = 60)

# }