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Title: Profile Plot: Mean QTc by Time and Treatment

ClinECGVitalsGraphicsForm edit

Image
Click on top image to enlarge r image and bottom one to enlarge SAS image.
Title Profile Plot: Mean QTc by Time and Treatment
Graph_Subgroup ECGVitals
ECG_Vitals_Clinical_Question 5. What is the relationship to drug concentration?
Contributor/Email Rich Anziano (richard.anziano@pharmetheus.com)
Max Cherny (Max.2.Cherny@gsk.com)
Background The figure presented here presents the mean profile over time from a tQt trial. This figure is intended to explore the effect of all four treatments. Since these are the same subjects in each of four treatment periods the data lend to creating a placebo adjusted change from baseline plot (often referred to as delta-delta plot).
Keywords tqt, profile plot, qtc, change from baseline
References

Datasets Other
CDISC_Data

Other_Data Anziano QTc Profiles ASCII File
CLASSIFICATIONS

Graph_Type Line Plot
Variable_Relationship

Data_Types

Special_Considerations

Code_Available Yes
Software Program R, SAS
Software R, SAS 9.2
R-Code - Attachment

R-Code #code courtesy of Max Cherny

rm(list = ls()) # clear objects
graphics.off() # close graphics windows

library(car) #initialize libraries
library(ggplot2) #initialize libraries
require(gridExtra) #Need gridExtra for gridtext function

#read in the data
bm <- read.table("ecg_vitals_G01.dat",
header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)

#initialize file name and path
somePDFPath = "ecg_vitals_G01.pdf"
pdf(file=somePDFPath, paper='A4r', width=15, height=8)

# format treatment code
bm$grouptxt <- factor(bm$group,
labels = c('Exp lo','Exp hi','Pbo','Moxi') )

#jitter x timepoints
bm$x<-bm$tpd
bm[bm$group==2,]$x <- bm[bm$group==2,]$x +0.02 #increase jittering distance as needed
bm[bm$group==3,]$x <- bm[bm$group==3,]$x +0.04 #increase jittering distance as needed
bm[bm$group==4,]$x <- bm[bm$group==4,]$x +0.06 #increase jittering distance as needed

#determine number of groups
number_of_trt<- as.numeric(max(unique(bm$group)))

##symbol of points
plotchar <- seq(c("1", "2", "3","4","5"))

#set up colors for each group
colors<-c("cyan", "blue","red", "black")

#set up margins
par(new=F, par(mar=c(14,5,8,1)))



#sort by group
bm <- bm[order(bm$group, bm$tpd ),]

#create dataset with unique groups
unique_trt<-subset(bm, duplicated(group))

#maximum Y
ymax=30

#Create one plot per each group in this loop
for (ii in 1:number_of_trt)
{

#create a temporary dataset for each group
bm_temp <- subset(bm, group==ii )
par(new=T) #make sure all plots are added together in a loop

#create empty plot. The points will be added later
plot( bm_temp$estimate~bm_temp$x,
ylim = c(-10, ymax) ,
type="n", #supress plot
xlab="",
yaxt = "n",
xaxt="n",
ylab="QTc Change (ms) - Fridericia Correction ",
xlim = c(0, 12)
)

#plot lines for each group
lines(bm_temp$x, bm_temp$estimate,
lwd=2,
ljoin=1,
lend=3,
lmitre=3,
lty=1,
col=colors[ii],
pch=plotchar[ii] )

##plot points for each group
points(bm_temp$x,
bm_temp$estimate,
cex=0.6, #size of points
col=colors[ii], #color of points
pch=plotchar[ii]) #symbol of points

temp <- subset(bm, group==ii )
temp_trt <- subset(unique_trt, group==ii )

#produce table of number of subjects
text(x=temp$tpd, y=(-ii*3)-25, temp$n, cex=0.8, xpd=T, col=colors[ii])
text(x=-1, y=(-ii*3)-25, temp_trt$grouptxt, cex=0.8, xpd=T, pos=4)

#plot whiskers
segments (x0=temp$x, x1=temp$x, y0=temp$lower, y1=temp$upper, col=colors[ii])
segments (x0=temp$x-0.1, x1=temp$x+0.1, y0=temp$upper, y1=temp$upper, col=colors[ii])
segments (x0=temp$x-0.1, x1=temp$x+0.1, y0=temp$lower, y1=temp$lower, col=colors[ii])

} #end of for (ii in 1:number_of_trt)

#display x axis
axis(1, tick=T, at=c(0, 0.5, 1, 2, 3, 4, 6, 9, 12),
labels = c('8:00', '8:30', '9:00', '10:00', '11:00', '12:00', '14:00', '17:00','20:00'))

#display y axis
yaxis_marks <- seq(-10,ymax,by=5)
axis(2, tick=T, at=yaxis_marks, las=2 ) #use las to display horizontal label on Y axis

#add reference line
abline (h=0, lty=2)


# add a legend
legend("bottom" ,
inset=-0.3,
xpd=T,
as.character( unique_trt$grouptxt), #text for legend
cex=1,
col=colors,
pch=plotchar,
ncol=number_of_trt,
title="Treatments:")




#x and y xaxis labels
text(x=mean(bm$tpd), y=-51, "Time of Day", cex=0.9, xpd=T)
text(x=-5, y=mean(bm$estimate)+20, "Placebo Adjusted QTc Change (milliseconds) - Fridericia Correction", cex=0.9, xpd=T, srt=90, pos=1)

# Add minor tick marks
library(Hmisc)
#nx number of intervals in which to divide the area between major tick marks on the X-axis. Set to 1 to suppress minor tick marks.
#ny same as nx but for the Y-axis
#tick.ratio ratio of lengths of minor tick marks to major tick marks. The length of major tick marks is retrieved from par("tck").
minor.tick(nx=1, ny=10)


#add text at fixed positions
grid.text(paste("user_id: ",toupper(format(Sys.time(), "%d%B%Y %H:%M"))), x=0.01, y=0.06,just="left")
grid.text("Protocol: text", x=0.01, y=0.93,just="left")
grid.text("Population: Text", x=0.01, y=0.9,just="left")
grid.text( paste("Page 1 of 1"), x=0.9, y=0.9,just="left")
grid.text(paste("ECG/VITALS G1"), x=0.5, y=0.88)
grid.text("Figure Number", x=0.5, y=0.85)
grid.text("Title2 (Base R)", x=0.5, y=0.82)



dev.off()
SAS-Code - Attachment Link to Matange QTc Profile SAS Code
SAS-Code

Stata-Code - Attachment

Stata-Code

Other Code - Attachment

Other Code

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Reference Image QTc_Profiles_r200.png
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Topic revision: r6 - 07 Jun 2021 - 13:35:50 - MaryBanach
 

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