QoLR (version 1.0.1)

plotTTD: Plot the Kaplan-Meier curve of the TTD or TUDD

Description

A program that plot the time to deterioration curves according to the Kaplan-Meier estimation method for all patients or according to treatment arm. Additional information can be added such as the number of patients at risk and the number of the cumulative events

Usage

plotTTD(time, event, group = NULL, nrisk = FALSE, nevent = FALSE, group.names = NULL,
 t = NULL, info = FALSE, pos.info = NULL, xlab, ylab)

Arguments

time

vector equals to the time to deterioration or the time to censure

event

a dummy vector equals to 1 if the patient is deteriorated and 0 if not

group

the name of the variable corresponding to the treatment arm, only if you want survival curves according to treatment arm. Only two groups are allowed

nrisk

Boolean equals to FALSE by default. If nrisk is TRUE, then the number of patients at risk is printed under the curve at each t time point.

nevent

Boolean equals to FALSE by default. If event is TRUE, then the number of cumulative events is printed under the curve at each t time point. In that case, you must also fix nrisk to TRU

group.names

if you want survival curves according to treatment arm, you must give the name of the treatment arms in the group.names vector

t

if nrisk is TRUE, you must give the time points to print the number of patients at risk in vector {t}

info

Boolean equals to FALSE by default. If two groups are given in the group vector, then the result of the Log-rank test and the Hazard ratio are added to the graph if info is TRUE

pos.info

the position of the Log-rank test and the Hazard ratio on the graph

xlab

a title for x axis

ylab

a title for y axis

Examples

#data(dataqol)
## Estimation of the scores using the 'scoring.QLQC30' function:
#score_dataqol=scoring.QLQC30(dataqol,id="Id",time="time")
## Merge of "score_dataqol" dataframe with the important 
## variables of the dataqol dataframe as follows:
#info=dataqol[,c("Id","time","date","death","Arm")]
#dataqol_final=merge(score_dataqol,info,by=c("Id","time"))

## Then we reorganized the obtained "dataqol_final" dataset in order that
## the "date" variable appeared in the third position:
#dataqol_final=dataqol_final[,c(1:2,18,3:17,19:20)]

## Finally, in order to apply the "TTD" function, the dataset has to be sorted by
## patient's identification number "Id" and HRQoL measures ("time"):
#dataqol_final=dataqol_final[order(dataqol_final$time),]
#dataqol_final=dataqol_final[order(dataqol_final$Id),]


## The following Figure corresponds to the TUDD of QoL score as compared to the
## baseline score with a 5-point MCID according treatment arm (arm effect).
## In this graph, we printed the number of patients still at risk at each time
## point according to treatment arm (nrisk=T). Moreover, the result of the log-rank
## test and the Hazard Ratio of arm 2 vs. arm 1 is also printed (info=T, pos.info=c(5,0.8)).
## The Hazard Ratio (arm 2 vs. arm 1) equals to 1.20 with 95% confidence interval
## (0.69-2.09) and the result of the log-rank test is p=0.523.

#tudd1=TUDD(dataqol_final, score="QL", MCID=5,ref.init="baseline",ref.def="def1")
#ttd_1=merge(tudd1,unique(dataqol_final[,c("Id","Arm")]))
#plotTTD(ttd_1$time.5.QL,ttd_1$event.5.QL,ttd_1$Arm,nrisk=T,nevent=F,
#group.names=c("Arm 1","Arm 2"), t=seq(0,10,2),info=T,pos.info=c(6,0.8),
#xlab="time (months)", ylab="probability (%)")