| getCP {lrstat} | R Documentation |
Conditional power allowing for varying parameter values
Description
Obtains the conditional power for specified incremental information given the interim results, parameter values, and data-dependent changes in the error spending function, as well as the number and spacing of interim looks.
Usage
getCP(
INew = NA_real_,
L = NA_integer_,
zL = NA_real_,
theta = NA_real_,
IMax = NA_real_,
kMax = NA_integer_,
informationRates = NA_real_,
efficacyStopping = NA_integer_,
futilityStopping = NA_integer_,
criticalValues = NA_real_,
alpha = 0.025,
typeAlphaSpending = "sfOF",
parameterAlphaSpending = NA_real_,
userAlphaSpending = NA_real_,
futilityBounds = NA_real_,
typeBetaSpending = "none",
parameterBetaSpending = NA_real_,
spendingTime = NA_real_,
MullerSchafer = 0L,
kNew = NA_integer_,
informationRatesNew = NA_real_,
efficacyStoppingNew = NA_integer_,
futilityStoppingNew = NA_integer_,
typeAlphaSpendingNew = "sfOF",
parameterAlphaSpendingNew = NA_real_,
typeBetaSpendingNew = "none",
parameterBetaSpendingNew = NA_real_,
spendingTimeNew = NA_real_,
varianceRatio = 1
)
Arguments
INew |
The maximum information of the secondary trial. |
L |
The interim adaptation look of the primary trial. |
zL |
The z-test statistic at the interim adaptation look of the primary trial. |
theta |
A scalar or a vector of parameter values of
length |
IMax |
The maximum information of the primary trial. |
kMax |
The maximum number of stages of the primary trial. |
informationRates |
The information rates of the primary trial. |
efficacyStopping |
Indicators of whether efficacy stopping is allowed at each stage of the primary trial. Defaults to true if left unspecified. |
futilityStopping |
Indicators of whether futility stopping is allowed at each stage of the primary trial. Defaults to true if left unspecified. |
criticalValues |
The upper boundaries on the z-test statistic scale for efficacy stopping for the primary trial. |
alpha |
The significance level of the primary trial. Defaults to 0.025. |
typeAlphaSpending |
The type of alpha spending for the primary trial. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, "user" for user defined spending, and "none" for no early efficacy stopping. Defaults to "sfOF". |
parameterAlphaSpending |
The parameter value of alpha spending for the primary trial. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD". |
userAlphaSpending |
The user defined alpha spending for the primary trial. Cumulative alpha spent up to each stage. |
futilityBounds |
The lower boundaries on the z-test statistic scale
for futility stopping for the primary trial. Defaults to
|
typeBetaSpending |
The type of beta spending for the primary trial. One of the following: "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, and "none" for no early futility stopping. Defaults to "none". |
parameterBetaSpending |
The parameter value of beta spending for the primary trial. Corresponds to rho for "sfKD", and gamma for "sfHSD". |
spendingTime |
The error spending time of the primary trial.
Defaults to missing, in which case, it is the same as
|
MullerSchafer |
Whether to use the Muller and Schafer (2001) method for trial adaptation. |
kNew |
The number of looks of the secondary trial. |
informationRatesNew |
The spacing of looks of the secondary trial. |
efficacyStoppingNew |
The indicators of whether efficacy stopping is allowed at each look of the secondary trial. Defaults to true if left unspecified. |
futilityStoppingNew |
The indicators of whether futility stopping is allowed at each look of the secondary trial. Defaults to true if left unspecified. |
typeAlphaSpendingNew |
The type of alpha spending for the secondary trial. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, and "none" for no early efficacy stopping. Defaults to "sfOF". |
parameterAlphaSpendingNew |
The parameter value of alpha spending for the secondary trial. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD". |
typeBetaSpendingNew |
The type of beta spending for the secondary trial. One of the following: "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, and "none" for no early futility stopping. Defaults to "none". |
parameterBetaSpendingNew |
The parameter value of beta spending for the secondary trial. Corresponds to rho for "sfKD", and gamma for "sfHSD". |
spendingTimeNew |
The error spending time of the secondary trial.
Defaults to missing, in which case, it is the same as
|
varianceRatio |
The ratio of the variance under H0 to the variance under H1. |
Value
The conditional power given the interim results, parameter values, and data-dependent design changes.
Author(s)
Kaifeng Lu, kaifenglu@gmail.com
References
Cyrus R. Mehta and Stuart J. Pocock. Adaptive increase in sample size when interim results are promising: A practical guide with examples. Stat Med. 2011;30:3267–3284.
See Also
Examples
# Conditional power calculation with delayed treatment effect
# Two interim analyses have occurred with 179 and 266 events,
# respectively. The observed hazard ratio at the second interim
# look is 0.81.
trialsdt = as.Date("2020-03-04") # trial start date
iadt = c(as.Date("2022-02-01"), as.Date("2022-11-01")) # interim dates
mo1 = as.numeric(iadt - trialsdt + 1)/30.4375 # interim months
# Assume a piecewise Poisson enrollment process with a 8-month ramp-up
# and 521 patients were enrolled after 17.94 months
N = 521 # total number of patients
Ta = 17.94 # enrollment duration
Ta1 = 8 # assumed end of enrollment ramp-up
enrate = N / (Ta - Ta1/2) # enrollment rate after ramp-up
# Assume a median survival of 16.7 months for the control group, a
# 5-month delay in treatment effect, and a hazard ratio of 0.7 after
# the delay
lam1 = log(2)/16.7 # control group hazard of exponential distribution
t1 = 5 # months of delay in treatment effect
hr = 0.7 # hazard ratio after delay
lam2 = hr*lam1 # treatment group hazard after delay
# Assume an annual dropout rate of 5%
gam = -log(1-0.05)/12 # hazard for dropout
# The original target number of events was 298 and the new target is 335
mo2 <- caltime(
nevents = c(298, 335),
allocationRatioPlanned = 1,
accrualTime = seq(0, Ta1),
accrualIntensity = enrate*seq(1, Ta1+1)/(Ta1+1),
piecewiseSurvivalTime = c(0, t1),
lambda1 = c(lam1, lam2),
lambda2 = c(lam1, lam1),
gamma1 = gam,
gamma2 = gam,
accrualDuration = Ta,
followupTime = 1000)
# expected number of events and average hazard ratios
(lr1 <- lrstat(
time = c(mo1, mo2),
accrualTime = seq(0, Ta1),
accrualIntensity = enrate*seq(1, Ta1+1)/(Ta1+1),
piecewiseSurvivalTime = c(0, t1),
lambda1 = c(lam1, lam2),
lambda2 = c(lam1, lam1),
gamma1 = gam,
gamma2 = gam,
accrualDuration = Ta,
followupTime = 1000,
predictTarget = 3))
hr2 = 0.81 # observed hazard ratio at interim 2
z2 = (-log(hr2))*sqrt(266/4) # corresponding z-test statistic value
# expected mean of -log(HR) at the original looks and the new final look
theta = -log(lr1$HR[c(1,2,3,4)])
# conditional power with sample size increase
getCP(INew = (335 - 266)/4,
L = 2, zL = z2, theta = theta,
IMax = 298/4, kMax = 3,
informationRates = c(179, 266, 298)/298,
alpha = 0.025, typeAlphaSpending = "sfOF")