SMARTAR (version 1.0.0)

seqmeans: Summarize sequence-specific descriptive statistics

Description

Return a message that lists all the treatment sequence embedded in SMART design and summarizes all the sequence-specific descriptive statistics. It also provide design diagram of SMART and graphs of sequence-specific descriptive statistics (boxplot for continuous primary outcome and barchart for binary primary outcome).

Usage

seqmeans(data, family = "normal", plot = "d")

Arguments

data

Input data frame of the sequential randomized trial (SMART) data used for analysis. The data should include the variables of stage-specific treatments (At; t=1,2,3), intermediate evaluation (Ot; t=1,2,3) and final primary outcome (Y), where t represent the number of stages embedded in design. If stage-1 treatment (A1) takes into account the information of baseline evaluation, O1 needed to be include in data, otherwise not.

family

A character string to specify the type of final primary outcome. The default is family=<U+201C>normal<U+201D>, which refers to the continuous primary outcome. If family=<U+201D>binomial<U+201D> then the primary outcome will be treated as binary variable.

plot

A character string to specify the output figure. If plot=<U+201D>d<U+201D> then output the design diagram of SMART; If plot=<U+201D>s<U+201D> then output boxplot for continuous primary outcome or bar plot for binary primary outcome by sequence. The default is plot=<U+201D>d<U+201D>.

Value

an object of information of all the treatment sequences and sequences-specific descriptive statistics defined in a SMART data

• SEQ: Index of treatment sequences.

• O1: Baseline evaluation outcome.

• A1: Action of stage-1 treatment.

• O2: Intermeidate outcome evaluated at the end of stage 1.

• A2: Action of stage-2 treatment.

• O3: Intermeidate outcome evaluated at the end of stage 2.

• A3: Action of stage-3 treatment.

• N: Number of subjects following a certain treatment sequence.

• MEAN: Sequence-specified sample mean.

• VAR: Sequence-specified sample variance.

References

Thall P., Millikan R. and Sung H.G. (2000), ``Evaluating multiple treatment courses in clinical trials,'' Statistics in Medicine, 19, 1011-1028

Lavori P. W. and Dawson R. (2000), ``A design for testing clinical strategies: biased adaptive within-subject randomization,'' Journal of the Royal Statistical Society, Series A, 163, 29-38.

Murphy, S. A. (2005), ``An experimental design for the development of adaptive treatment strategies,<U+201D> Statistics in Medicine, 24, 1455-1481.

Examples

# NOT RUN {
#generate a pesudo SMART data
N=8000                       # total sample size
A1=O2=A2=Y=rep(NA,N)
Dat=data.frame(A1,O2,A2,Y)

# stage-1 treatment
Dat$A1=sample(c(0,1),size=N,prob=c(1,1),replace=TRUE)

# intermediate outcome
n0=length(Dat$A1[which(Dat$A1==0)]); n1=N-n0
Dat$O2[which(Dat$A1==0)]=rbinom(n=n0,size=1,p=0.6)
Dat$O2[which(Dat$A1==1)]=rbinom(n=n1,size=1,p=0.4)

# stage-2 treatment
n00=nrow(Dat[which(Dat$A1==0 & Dat$O2==0),]); n01=n0-n00
n10=nrow(Dat[which(Dat$A1==1 & Dat$O2==0),]); n11=n1-n10
Dat$A2[which(Dat$A1==0 & Dat$O2==0)]=sample(c(0,1),size=n00,prob=c(1,1),replace=TRUE)
Dat$A2[which(Dat$A1==0 & Dat$O2==1)]=sample(c(0,1),size=n01,prob=c(1,1),replace=TRUE)
Dat$A2[which(Dat$A1==1 & Dat$O2==0)]=sample(c(0,1),size=n10,prob=c(1,1),replace=TRUE)
Dat$A2[which(Dat$A1==1 & Dat$O2==1)]=sample(c(0,1),size=n11,prob=c(1,1),replace=TRUE)

n000=nrow(Dat[which(Dat$A1==0 & Dat$O2==0 & Dat$A2==0),]); n001=n00-n000
n010=nrow(Dat[which(Dat$A1==0 & Dat$O2==1 & Dat$A2==0),]); n011=n01-n010
n100=nrow(Dat[which(Dat$A1==1 & Dat$O2==0 & Dat$A2==0),]); n101=n10-n100
n110=nrow(Dat[which(Dat$A1==1 & Dat$O2==1 & Dat$A2==0),]); n111=n11-n110

# final primary outcome
Dat$Y[which(Dat$A1==0 & Dat$O2==0 & Dat$A2==0)]=rnorm(n=n000,mean=7,sd=6)
Dat$Y[which(Dat$A1==0 & Dat$O2==0 & Dat$A2==1)]=rnorm(n=n001,mean=7,sd=6)
Dat$Y[which(Dat$A1==0 & Dat$O2==1 & Dat$A2==0)]=rnorm(n=n010,mean=7,sd=8)
Dat$Y[which(Dat$A1==0 & Dat$O2==1 & Dat$A2==1)]=rnorm(n=n011,mean=7,sd=8)
Dat$Y[which(Dat$A1==1 & Dat$O2==0 & Dat$A2==0)]=rnorm(n=n100,mean=7,sd=6)
Dat$Y[which(Dat$A1==1 & Dat$O2==0 & Dat$A2==1)]=rnorm(n=n101,mean=7,sd=6)
Dat$Y[which(Dat$A1==1 & Dat$O2==1 & Dat$A2==0)]=rnorm(n=n110,mean=7,sd=8)
Dat$Y[which(Dat$A1==1 & Dat$O2==1 & Dat$A2==1)]=rnorm(n=n111,mean=7,sd=8)

#get descriptive statistics
seqmeans(data=Dat,family="normal",plot="d")

# }