Type:	Package
Title:	Creates Group Sequential Trial Designs when Early Outcomes are Available
Version:	1.0.0
Maintainer:	Nick Parsons <nick.parsons@warwick.ac.uk>
Description:	Methods to construct and power group sequential clinical trial designs for outcomes at multiple times. Outcomes at earlier times provide information on the final (primary) outcome. A range of recruitment and correlation models are available as are methods to simulate data in order to explore design operating characteristics. For more details see Parsons (2024) <doi:10.1186/s12874-024-02174-w>.
Depends:	R (≥ 4.1.2)
Imports:	graphics, grDevices, gsDesign (≥ 3.2.1), methods, mvtnorm (≥ 1.2-4), nlme (≥ 3.1-160), stats, utils
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
LazyData:	true
NeedsCompilation:	no
Packaged:	2026-01-20 13:31:42 UTC; nickr
Author:	Nick Parsons [aut, cre]
Repository:	CRAN
Date/Publication:	2026-01-23 21:20:12 UTC

Group sequential designs with early outcomes

Description

Functions to implement group sequential clinical trial designs with early outcomes. Group sequential designs are one of the most widely used methodologies for adaptive design in randomized clinical trials. In such designs researchers collect data and undertake sequential analyses with the opportunity to either reject the null hypothesis, stop the study for futility or continue recruitment at an interim look, before reaching the planned sample size.

In situations where, for instance, outcomes are collected at long follow-up time-points, data at interim analyses are often available for not only the study primary (long-term) outcome time-point but also from early time-points for the same outcome (early outcomes); e.g. a primary outcome at 12 months and early outcomes at 3, 6 and 9 months. In settings where moderate to strong correlations exist between the sequence of such outcomes, information can be used from the early outcomes in addition to the final outcome at the interim analyses. The design, planning and sample size determination for such studies is more complex than for conventional group sequential designs and is generally achieved by simulating individual participant data for an assumed recruitment pattern as a means to determine information accrual during a proposed trial.

However, in practice, such simulations are complex and time-consuming to set-up and implement and provide a barrier to the use of group sequential designs. If we can assume approximate multivariate Normality for the distribution of the outcomes, and also make some assumptions about the expected correlation structure and recruitment patterns, then we can derive relatively simple analytic expressions for information accrual during a planned trial. Allowing a range of design options to be explored routinely without the burden of undertaking extensive simulation studies.

Details

The two main functions (i) gsearlyModel and (ii) gsearlyUser allow designs to be constructed based on a range of typical clinical trial recruitment patterns and correlation models. The function gsearlySimulate simulates multivariate Normal datasets based on a previously fitted gsearly model.

References

Parsons NR, Basu J, Stallard N. Group sequential designs for pragmatic clinical trials with early outcomes: methods and guidance for planning and implementation. BMC Medical Research Methodology 2024; 24:42. https://wrap.warwick.ac.uk/id/eprint/183449/

Parsons NR, Stallard N, Parsons H, Haque A, Underwood M, Mason J, Khan I, Costa ML, Griffin DR, Griffin J, Beard DJ, Cook JA, Davies L, Hudson J, Metcalfe A. Group sequential designs in pragmatic trials: feasibility and assessment of utility using data from a number of recent surgical RCTs. BMC Medical Research Methodology 2022; 22:256. https://wrap.warwick.ac.uk/id/eprint/169801/

See Also

gsearlyModel, gsearlyUser, gsearlySimulate

Exponential correlation matrix

Description

Constructs an exponential correlation matrix.

Usage

corrExp(rho=0, tfu)

Arguments

Value

Returns an s x s correlation matrix. Where s is the number of occasions (follow-up time points) at which the study outcome is observed.

Examples

 # Exponential correlation model for outcomes at 3, 6, 12 and 24 months
 # Based on correlation 0.5 between outcomes separated by 3 month
 corrExp(0.5, tfuStandard(c(3,6,12,24), tref=c(1,2)))

 # Based on correlation 0.25 between outcomes separated by 1 year
 corrExp(0.25, tfuStandard(c(3,6,12,24), tref=c(3,4)))

Uniform correlation matrix

Description

Constructs a uniform correlation matrix.

Usage

corrUnif(rho=0, tfu)

Arguments

Value

Returns an s x s correlation matrix. Where s is the number of occasions (follow-up time points) at which the study outcome is observed.

Examples

 # Uniform correlation model for outcomes at 3, 6, 12 and 24 months
 # Settings for tref argument of standardtfu do not change correlation matrix
 corrUnif(0.5, tfuStandard(c(3,6,12,24), tref=c(1,2)))

 # Based on correlation 0.5 between outcomes separated by 1 year
 corrUnif(0.5, tfuStandard(c(3,6,12,24), tref=c(3,4)))

Order a data frame by subject and ordered time variable

Description

Orders a user supplied data frame by subject and ordered time variable.

Usage

dataOrder(data, datanames=c("id","atime","catime",
                                            "intervention","outcome"))

Arguments

Value

Returns an ordered data frame.

Examples

 data(qol)
 oqol <- dataOrder(qol,datanames=c("Subject","Weeks","STime","Treat","QoL"))
 head(qol, n=50)

Expected sample size for a gsearly model

Description

Calculates the expected sample size for a fitted gsearly model, from functions gsearlyModel or gsearlyUser.

Usage

expectSampsize(mod, signif=3)

Arguments

Value

Returns the sample size for the control and treatment groups and total.

See Also

gsearlyModel, gsearlyUser

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0010,0.0100,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="fix", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8, cmodel="exponential", sd=20,
                   rho=0.75, theta=8, fp=fp, tn=tn)
 expectSampsize(rctdesign)

Fixed model sample size for a gsearly model

Description

Calculates the fixed model sample size, assuming no correlation between early and final outcomes, for a fitted gsearly model, from functions gsearlyModel or gsearlyUser.

Usage

fixedSampsize(mod, pow=NULL, direct="u", signif=3)

Arguments

Value

Returns a list with the following items.

See Also

gsearlyModel, gsearlyUser

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="fix", trecruit=36, s=3, tfu=c(3,6,12),
              tinterims=c(16,31), pow=0.8, cmodel="exponential", sd=20,
              rho=0.75, theta=8, fp=fp, tn=tn)
 fixedSampsize(rctdesign)

Fit a generalized least squares model

Description

Fits a generalized least squares model using function gls (from package nlme)

Usage

gsearlyFit(data, datanames = c("id","atime","catime",
      "intervention","outcome"), cmodel=FALSE, vmodel=TRUE, full=FALSE)

Arguments

Value

Either a gls model (full=TRUE) or a summary of the model fit (full=FALSE), which is a list with the components.

See Also

gsearlySimulate, simdataExtract, modelParameters

Examples

 # Model for full data set
 data(qol)
 mod.fulldata <- nlme::gls(QoL~(Weeks-1)+(Weeks-1):Treat, data=qol,
    correlation=nlme::corSymm(form=~1|Subject),
    weights=nlme::varIdent(form =~1|Weeks), method="ML")
 summary(mod.fulldata)

 # Use data available at day 150
 data.interim1 <- qol[qol$Day<=150,]
 mod.interim1 <- nlme::gls(QoL~(Weeks-1)+(Weeks-1):Treat, data=data.interim1,
    correlation=nlme::corSymm(form=~1|Subject),
    weights=nlme::varIdent(form=~1|Weeks), method="ML")
 summary(mod.interim1)

 # Use glsFit
 gsearlyFit(qol, datanames=c("Subject","Weeks","STime","Treat","QoL"))
 # vbeta
 vcov(mod.fulldata)[6,6]
 # beta
 coef(mod.fulldata)[6]

Model based estimates of sample size and power for group sequential designs with early outcomes

Description

Provides sample sizes and power for group sequential designs with early outcomes defined by a recruitment and correlation model, recruitment period and interim analysis time-points.

Usage

gsearlyModel(rmodel="fix", trecruit, s, tfu, tinterims, pow=0.9, n=NULL,
        theta, tref=c(1,2), vphi=0.5, m=2, cmodel="uniform", sd=1, rho=0.5,
        fp, tn, treatnames=c("control", "treat"),
        sopt=list(r=18, bisect=list(min=20, max=10000, niter=1000, tol=0.001)))

Arguments

Value

An object of class gsearly is a list containing the following components.

See Also

expectSampsize, fixedSampsize, roundInterims, corrExp, corrUnif

Examples

 # RCT planning:

 # Recruitment is expected to take 36 months (trecruit) with three
 # follow-up times (s) for outcomes at 3, 6 and 12 months (tfu) and
 # interim analyses (tinterims) at 18 and 30 months.
 # Study procedures and experience suggest that "dilin" (m=2) is likely
 # to be a good approximation to temporal patterns of recruitment and data
 # accrual (rmodel). Correlations between outcomes at 3, 6 and 12 are
 # assumed to follow a uniform model (cmodel) with parameter  (rho) set to 0.5.
 # Randomisation to the two study arms will be on a 1:1 basis (vphi=0.5).
 # The standard deviation of the final 12m outcome is expected to be 20,
 # and the anticipated treatment effect (theta) 8.

 # Primary interest for the RCT is in stopping early for futility.
 # Therefore the following values for the false positive and true
 # negative rates were selected.
 # Vectors of cumulative probabilities for crossing boundaries under the
 # null hypothesis (of no treatment difference).
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, vphi=0.5, m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)
 rctdesign

 # If n is set, rather than pow, then we can explore power for lower n
 update(rctdesign, n=136)

 # Expected numbers of participants at interim analyses
 rctdesign$rdata$interims

 # Information at these interims and final analysis
 rctdesign$idata$interims

 # Upper and lower stopping boundaries and probabilities
 rctdesign$power$lowerror
 rctdesign$power$upperror

Simulates data for a previously fitted gsearly model

Description

Simulates multivariate Normal datasets based on a previously fitted gsearly model (see gsearlyModel).

Usage

gsearlySimulate(mod, nsim=1, minsamp=c(1,1), mean=NULL, cmodel=NULL,
          sd=NULL, rho=NULL, full=FALSE)

Arguments

Value

A list containing the following components.

See Also

gsearlyModel, simdataExtract, modelParameters, gsearlyFit

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, vphi=0.5, m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)

 # Simulate data from this model
 simdata <- gsearlySimulate(mod=modeldesign, nsim=10, full=FALSE)
 # Model parameters
 simdata$parameters

 # Change correlation model
 newsimdata <- gsearlySimulate(mod=modeldesign, nsim=10,
                    cmodel="exponential", rho=0.75, full=FALSE)
 # Model parameters
 newsimdata$parameters

User input estimates of sample size and power for group sequential designs with early outcomes

Description

Provides sample sizes and power for group sequential designs with early outcomes defined by a matrix of fixed numbers of participants with data for all outcomes (early and primary) at each interim time-point or a function that gives a similarly structured matrix for all sample sizes in a pre-set range and a correlation model or correlation matrix, recruitment period and interim analysis time-points.

Usage

gsearlyUser(trecruit, s, tfu, tinterims, ninterims, pow=0.9, n=NULL,
       tref=c(1,2), vphi=0.5, cmodel="uniform", sd=1, rho=0.5, theta, fp, tn,
       treatnames=c("control","treat"),
       sopt=list(r=18, bisect=list(min=20, max=10000, niter=1000, tol=0.001)))

Arguments

Value

An object of class gsearly is a list containing the following components

See Also

gsearlyModel

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, vphi=0.5, m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)
 modeldesign

 # This design can be replicated using gsearlyUser
 n <- modeldesign$rdata$n["total"]
 ninterims <- modeldesign$rdata$interims
 cmodel <- modeldesign$idata$cmodel$corrmat
 userdesign <- gsearlyUser(trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(18,30),
        ninterims=ninterims, n=n, vphi=0.5, cmodel=cmodel,
        sd=20, theta=8, fp=fp, tn=tn)
 userdesign

 # Expected numbers of participants at interim analyses
 modeldesign$rdata$interims
 userdesign$rdata$interims

 # Information at these interims and final analysis
 modeldesign$idata$interims
 userdesign$idata$interims

 # Upper and lower stopping boundaries and probabilities
 rbind(modeldesign$power$lowerror, modeldesign$power$upperror)
 rbind(userdesign$power$lowerror, userdesign$power$upperror)

 # Change correlation matrix and interim numbers
 cmodel <- matrix(c(1,0.2,0.1, 0.2,1,0.1, 0.1,0.1,1), nrow=3, byrow=TRUE)
 ninterims <- matrix(c(130,110,90,45, 200,175,160,120), nrow=2, byrow=TRUE)
 # For 90 percent power (pow), a call to gsearlyUser provides a feasible design
 nuserdesign <- gsearlyUser(trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(18,30),
        ninterims=ninterims, vphi=0.5, pow=0.9, cmodel=cmodel,
        sd=20, theta=8, fp=fp, tn=tn)
 nuserdesign

Estimates model parameters from raw data

Description

Provides estimates of the variance of beta (vbeta) and beta, the treatment effect, directly from raw data and a user supplied covariance matrix.

Usage

modelParameters(data, datanames=c("id", "atime", "intervention", "outcome"),
              vcovmat)

Arguments

Value

Returns the variance of beta (vbeta), beta and test statistic z.

See Also

gsearlySimulate, simdataExtract

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, vphi=0.5, m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)

 # Simulate data from this model with raw data using full=TRUE
 simdata <- gsearlySimulate(mod=modeldesign, nsim=10, full=TRUE)

 # Extract raw data for a single simulation
 simdat1 <- simdataExtract(simdata, simn=1, tinterims=18, full=TRUE)
 # Get model parameters
 modelParameters(data=simdat1$data, vcovmat=simdat1$model$covariance)

 # Try alternative covariance model
 varmat <- diag(c(18,22,24))
 vcovmat <- tcrossprod(crossprod(varmat,corrExp(rho=0.8,
                                            tfu=simdat1$model$tfu)),varmat)
 modelParameters(data=simdat1$data, vcovmat=vcovmat)

Plot a gsearly model

Description

Plots data from a gsearly model constructed using either gsearlyModel or gsearlyUser.

Usage

## S3 method for class 'gsearly'
plot(x, plottype=1, ...)

Arguments

Value

A plot of the selected type.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 plot(rctdesign, "recruit")

A boundary plot for a gsearly model

Description

A boundary plot for a gsearly model.

Usage

plotBoundary(mod, xlim=c(0,1), ylim=NA, xlab=NA, ylab=NA, title=NULL, col=1,
    lty=c(3, 3), pch=3, las=1, concol=grey(0.9), reglab=TRUE,  signif=3,
    bounds=TRUE, pcol=1, labpos=c(2,2), ...)

Arguments

Value

A plot of the selected type or a list consisting of interim information fractions and lower and upper boundaries.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="fix", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 plotBoundary(rctdesign)

An information plot for a gsearly model

Description

An information or variance plot for a gsearly model.

Usage

plotInform(mod, xlim=NA, ylim=NA, xlab=NA, ylab=NA, title=NULL, col=1,
    lty=1, pch=3, las=1, tlag=c(0,0), wopcol=grey(0.9), intlab=NA, labpos=1,
    legpos="bottomleft", ptype="var", pcol=1, ...)

Arguments

Value

A plot of the selected type or a list consisting of the plotted line and interim point data.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 plotInform(rctdesign)

A power plot for a gsearly model

Description

A power plot for a gsearly model.

Usage

plotPower(mod, xlim=NA, ylim=NA, xlab=NA, ylab=NA, title=NULL,
    col=c(1,2), lty=NA, pch=3, las=1, legpos=c("bottomright","topright"),
    xtype="theta", delta=seq(0,1,0.05), legstudy=NA, ...)

Arguments

Value

A plot of the selected type or a list consisting of the plotted line and interim point data.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0010,0.0100,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 plotPower(rctdesign)

A recruitment plot for a gsearly model

Description

A recruitment plot for a gsearly model.

Usage

plotRecruit(mod, xlim=NA, ylim=NA, xlab=NA, ylab=NA, title=NULL, col=NA,
    lty=c(1,2), pch=3, las=1, wopcol=grey(0.9), intlab=NA, labpos=3,
    legpos="topleft", tlag=c(0,0), pcol=1, ...)

Arguments

Value

A plot of the selected type or a list consisting of the plotted line and interim point data and bounds for the window of opportunity.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 plotRecruit(rctdesign)

Print a gsearly model

Description

Prints gsearly model details.

Usage

## S3 method for class 'gsearly'
print(x, digits=4, ...)

Arguments

Value

A list containing the following components.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 rctdesign
 print(rctdesign)
 str(print(rctdesign))

Print a gsearly model summary

Description

Prints a gsearly model summary.

Usage

## S3 method for class 'gsearly'
print.summary(x, digits=4, ...)

Arguments

Value

A list containing the following components

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 summary(rctdesign)
 str(summary(rctdesign))

Quality of life index for patients with knee pain

Description

Quality of life scores for 175 participants, from a randomized controlled trial, at 2, 4 and 8 weeks after treatment.

Usage

data(qol)

Format

A data frame with 455 observations on the following four variables.

Subject

A participant identifier variable.

Weeks

Recorded at follow-up times of 2, 4 and 8 weeks.

STime

Standardized follow-up time variable; see tfuStandard.

Treat

Random treatment allocation (A or B).

QoL

Quality of life index (0-100), where a higher value indicates a higher QoL.

Day

Day number (starting at 1), when QoL was assessed.

Examples

 data(qol)
 head(qol)

Round a gsearly design interim sample size to integer values

Description

Rounds the interim sample size to integer values.

Usage

roundInterims(mod, direct="u", full=FALSE)

Arguments

Value

Returns a matrix (or matrices, if full=TRUE) of the total sample sizes (and control and treatment groups, if full=TRUE) for each outcome at interims analyses.

See Also

gsearlyModel, gsearlyUser

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="fix", trecruit=36, s=3, tfu=c(3,6,12),
                  tinterims=c(16,31), pow=0.8,
                  cmodel="exponential", sd=20, rho=0.75, theta=10, fp=fp, tn=tn)
 rctdesign

 # Expected numbers of participants at interim analyses
 rctdesign$rdata$interims

 # Round design up to integer values
 round_rctdesign <- roundInterims(rctdesign, direct="u")
 round_rctdesign

 # Power for rounded design
 n <- rctdesign$rdata$n["total"]
 ninterims <- round_rctdesign
 cmodel <- rctdesign$idata$cmodel$corrmat
 userdesign <- gsearlyUser(trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(16,31),
        ninterims=ninterims, n=n, cmodel=cmodel,
        sd=20, theta=10, fp=fp, tn=tn)
 userdesign
 userdesign$rdata$interims

Extract simulated data for a single trial

Description

Extract data created using function gsearlySimulate.

Usage

simdataExtract (simmod, simn, tinterims,
         datanames=c("id", "atime", "catime", "intervention", "outcome"),
         full=FALSE)

Arguments

Value

A list containing the following components.

See Also

gsearlySimulate

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, vphi=0.5, m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)

 # Simulate data from this model with raw data using full=TRUE
 simdata <- gsearlySimulate(mod=modeldesign, nsim=10, full=TRUE)

 # Extract raw data for a single simulation
 simdat1 <- simdataExtract(simdata, simn=1, tinterims=18,
          full=TRUE, datanames=c("ID","Time", "cTime", "Treat", "Outcome"))
 head(simdat1$data,n=20)

Summarise a gsearly model

Description

Provides a summary of a gsearly model.

Usage

## S3 method for class 'gsearly'
summary(object, ...)

Arguments

Value

A gsearly model summary.

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.4800,0.7200,0.9750)
 rctdesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(16,31), pow=0.8,
                   cmodel="exponential", sd=20, rho=0.75, theta=8, fp=fp, tn=tn)
 summary(rctdesign)
 str(summary(rctdesign))

Standardize follow-up times

Description

Standardize the follow-up (early) time-points.

Usage

tfuStandard(tfu, tref=c(1,2))

Arguments

Value

A list containing the following components.

Examples

 # Early outcomes at 3, 6 and 12 months, and final at 24 months
 # Standardized to units of 3 months if tref is c(1,2)
 tfuStandard(c(3,6,12,24), tref=c(1,2))

 # Standardized to yearly units if tref is c(1,2)
 tfuStandard(c(3,6,12,24), tref=c(3,4))