power.ROCI.binary: Function to compute the power for a given sample size for a...
| power.ROCI.binary | R Documentation |
Function to compute the power for a given sample size for a MAMS-ROCI (aka DURATIONS) randomised trial.
Description
A function that can be used to do power calculations for a MAMS-ROCI randomised trial with binary outcome. The trial can aim to use one of several possible analysis methods to find the minimum/maximum non-inferior arm.
Usage
power.ROCI.binary(p.expected.curve, NI.margin, reference=max(treatment.levels),
optimal=min(treatment.levels), range=optimal, unfavourable=T,
se.method="bootstrap", treatment.levels, summary.measure="RD",
tr.model="FP2.classic", M.boot=NULL, parallel="no", n.cpus=1, sig.level=0.025,
n.per.arm, print.out=T, simulations=FALSE, n.rep=1000, bootCI.type="bca")
Arguments
p.expected.curve |
The expected event risks at the specified treatment levels. It should be a numeric vector of the same length as treatment.levels. |
NI.margin |
The non-inferiority margin(s). It can be either a scalar, if the margin is the same for all treatment levels, or a vector of length equal to that of treatment.levels minus one. For summary.measure="target.risk", this represents the target probability that we want to achieve, and if given as a vector it has length equal to that of treatment.levels. |
reference |
The arm that should act as a reference. It must be one of the arms in treatment levels. Default is the largest value among treatment.levels. |
optimal |
The arm that should be considered as the optimal under the given data generating mechanism. It must be one of the arms in treatment levels. Default is the smallest value among treatment.levels. |
range |
The arm(s) that should be considered in the acceptable range for the range power method under the given data generating mechanism. It must be one or more of the arms in treatment levels. Default is the smallest value among treatment.levels (same as optimal). |
unfavourable |
If TRUE the outcome is taken to be an unfavourable event, e.g. death or relapse. If FALSE, the outcome is considered a positive one, like cure. |
se.method |
Method for calculating standard errors around the treatment-response curve. Can be either "bootstrap" or "delta". |
treatment.levels |
A vector with all the treatment levels to be investigated. These will generally correspond to the treatment arms. |
summary.measure |
The population-level summary measure that defines the estimand. It can be either "RD" (Risk Difference), "RR" (Risk Ratio), "target.risk" (target probability) or "OR" (Odds Ratio). |
tr.model |
Model to use for the treatment-response curve. It can be one of: "FP1.classic" or "FP2.classic" (Multivariable Fractional Polynomials as in package mfp, with a maximum of either 1 or 2 powers), "FP1.fixed" or "FP2.fixed" (Fractional polynomials fixing the number of powers to exactly 1 or 2). |
M.boot |
Number of bootstrap samples if using se.method="bootstrap". |
parallel |
If using bootstrap, this gives the chance to parallelise calculations. Use parallel="snow" with windows, or parallel="multicore" with Linux. See help page for the boot function. |
n.cpus |
If using bootstrap in parallel, this gives teh number of cpus to parallelise computations on. |
sig.level |
One-sided significance level. |
n.per.arm |
The sample size (number of patients per arm) for which we want to compute power. It can either be a scalar, assuming same sample size in all treatment.levels, or a vector of length equal to treatment.levels. |
print.out |
If TRUE the estimated sample sizes are printed on screen. |
simulations |
A logical variable. If set to TRUE, power is estimated by running simulations. Otherwise, a modified version of the formula in samplesize.ROCI.binary is used. Default is FALSE. |
n.rep |
The number of repetitions to be used for the simulations if simulations=TRUE. |
bootCI.type |
Method for computing the confidence intervals if using se.method="bootstrap" and simulations=TRUE. It can be either "norm", "basic", "perc" or "bca". Default is "bca", which is the recommended option when possible. |
Details
This function computes optimal, range and acceptable power for a ROCI trial with a given sample size. It requires as input the expected treatment response curve under which to power the trial, the NI margins and a specification of all the methods. It can estimate power either using a formula or simulations. If using formula, the function creates a data set of expected outcomes (e.g. if risk in one arm is 0.7, it creates 70 records with event=1 and 30 with event=0) and fits the model for the treatment-response curve on this data set. It then estimates the variance of the summary measure of interest and uses this to do a power calculation based on simple formula from normal theory for non-inferiority with a continuous outcome. If using simulations, the function runs n.rep simulations with the specified sample size and analysis methods. The function allows for the margin to be expressed as either a risk difference, risk ratio, odds ratio or a target probability.
Value
A list including computed optimal, range and acceptable power. Acceptable power is estimated as the maximum power with the given sample size for any of the treatment levels. The function also returns power for all specific treatment levels.
Examples
p.expected.curve=rep(0.05,7)
NI.margin=rep(0.05,6)
reference=20
optimal=8
range=c(8,10,12)
se.method="delta"
treatment.levels<-c(8,10,12,14,16,18,20)
summary.measure<-"RD"
tr.model="FP2.classic"
sig.level=0.05
unfavourable=T
ss<-power.ROCI.binary(p.expected.curve, NI.margin, reference=reference, optimal=optimal, range=range, unfavourable=unfavourable,
se.method=se.method,treatment.levels=treatment.levels,summary.measure=summary.measure,
tr.model=tr.model, sig.level = sig.level, n.per.arm = 150)
