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StatClassForm edit

Title Dot plots
Graph Displayed class
Graph Subgroup Adverse Events
Classification-Graph Type Dotplot
Date Original
Original Date June 15, 2011
Modified Date

Contributor/Email Mat Soukup (email: Mat.Soukup@fda.hhs.gov)
Contributor1 Modification/Email

Contributor2 Modification/Email

Contributor3 Modification/Email

Disclaimer The opinions expressed in this document are those of the author and may not represent the opinions of the U.S. Food and Drug Administration or other authors.
Type of Data Continuous
Type of Analysis Univariate
Description and Purpose The dotplot is a useful graphical approach for plotting a quantitative variable with labels representing the quantitative measurement (e.g., when looking at adverse event rates, a label for the adverse event accompanies the rate). The dotplot is an alternative to plotting data using such displays as bar charts and pie charts, though it has advantages over such approaches in that it allows one the ability to plot data in which the baseline of the data is not zero.

Comparison of Multiple Graphing Techniques
To illustrate the use of multiple graphical displays suppose we want to illustrate the disposition of subjects at the end of trial. Table 1 depicts the disposition along with the percentage of subjects in each category.


Table 1: Disposition
| Disposition | Percentage |
| Completed Study | 75.0% |
| Adverse Event | 7.0% |
| Lack of Efficacy | 8.0% |
| Withdrew Consent | 6.0% |
| Other | 4.0% |
Datasets CDISC Datasets
Data Data Preparation Code for ADAE CDISC Pilot Data set
adae <- read.xport("C:/Research/CDISC-ADaMPilot/900171/m5/datasets/CDISCPILOT01/analysis/ADAE.xPT",
names.tolower=TRUE)
adsl <- read.xport("C:/Research/CDISC-ADaMPilot/900171/m5/datasets/CDISCPILOT01/analysis/ADSL.xPT",
names.tolower=TRUE)
dat <- data.frame(ID=adae$usubjid,PT=adae$aedecod,TRT=adae$trtp)

# For each subject get only the unique PT
uid <- unique(adae$usubjid)
pts <- NULL
trts <- NULL
for(i in 1:length(uid)){
sdat <- subset(dat, ID%in%uid[i])
upt <- unique(sdat$PT)
npt <- length(upt)
rows <- NULL
for(k in 1:npt){
ww <- which(sdat$PT%in%upt[k])
rows[k] <- ww[1]
}
pts[i] <- list(sdat$PT[rows])
trts[i] <- list(sdat$TRT[rows])
}

dat1 <- data.frame(PT=unlist(pts), TRT=unlist(trts))

ss <- with(dat1, table(PT, TRT))
sums <- apply(ss, 1, sum)
ww <- which(sums>0)
xPT <- ss[ww,]

# Get percents
pPT <- NULL
for(j in 1:dim(xPT)[1]){
pPT[j] <- list(100*xPT[j,]/c(84,84,84)) #N/group come from ADSL
}
matP <- round(do.call('rbind', pPT),1)

# remove PT which occur in less than p subjects
sump <- apply(matP, 1, sum)
wsum <- which(sump > 5.0)

matXp <- xPT[wsum,]
matPp <- matP[wsum,]
rownames(matPp) <- rownames(matXp)
colnames(matPp) <- colnames(matXp)

plotdat <- data.frame(PT = rep(rownames(matPp), 3),
TRT = rep(colnames(matPp), each=dim(matPp)[1]),
PCT = c(matPp[,1], matPp[,2], matPp[,3]))

Example1Title Figure 1: Disposition Graphical Displays
Example1Description Figure 1 depicts the data from Table 1 using three approaches: dotplot, pie chart, and barplot. In this example, we can see the limitations of the pie chart as it is difficult to interpret the numerical differences for subjects that did not complete the study. Both the dotplot and the barchart show the magnitude of the differences. However, the dotplot has an advantage over the barchart in that the labels are written horizontally for the disposition as well as the ink used in the plotting region only corresponds to the point of interest, in this case the percentage.
Example1Image
Click on figure for enlarged image
Example2Title Figure 2: Percentage of subjects reporting an adverse event (MedDRA? preferred term)
Example2Description Application of Dotplots to Adverse Event Data

As an illustrative example, we can plot the percent of subjects that experience an adverse event (using MedDRA preferred terms) in a clinical trial (Figure 2). In this example, we see that the preferred terms are listed in alphabetical order.

Example2Image
Click on figure for enlarged image
Example3Title Figure 3: Percentage of subjects reporting an adverse event (MedDRA preferred term) sorted by Frequency
Example3Description To aid in the visualization, the ordering of the preferred terms can be done based on the corresponding percentage (Figure 3). This plot allows one to easily see that PRURITUS and APPLICATION SITE PRURITUS are reported in the most frequently which requires more inspection if using Figure 2.
Example3Image
Click on figure for enlarged image
Example4Title Figure 4: Percentage of subjects reporting an adverse event (MedDRA preferred term) sorted by Frequency and System Organ Classification
Example4Description In addition, to using a basic dotplot as shown in Figures 2 and 3, additional information can be incorporated into the dotplot. For example, we could sort, the preferred according to the system organ classification (Figure 4). In this figure, color is used to denote the different the location of the SOC and terms are plotted by SOC descending from most frequent PT. Note that in this case, the color could be suppressed and rather the graphic could be printed in black and white since the SOC is provided on left side of the graphic.
Example4Image
Click on figure for enlarged image
Example5Title Figure 5: Percentage of subjects reporting an adverse event (MedDRA preferred term) sorted by frequency and grouped by treatment assignment
Example5Description The dotplot also allows one to use different plotting characters to correspond to a grouping (i.e. a superposition of plotting symbols). In the clinical trial realm, this would often times correspond to the treatment assignment. Figure 5 is similar in appearance to Figure 3 but adding in information for alternate treatment arms.
Example5Image
Click on figure for enlarged image
Example6Title Figure 6: Percentage of subjects reporting an adverse event (MedDRA preferred term) sorted by frequency and grouped by treatment assignment with panels for treatment relationship
Example6Description To incorporate additional information into the dotplot, one can also panel the display on additional categorical variable. Figure 6 contains such a plot in which the preferred term is paneled on the investigator’s determination of whether the event was related to treatment or not.
Example6Image
Click on figure for enlarged image
Example7Title

Example7Description

Example7Image

Example8Title

Example8Description

Example8Image

Example9Title

Example9Description

Example9Image

References Reference: Cleveland’s “The Elements of Graphing Data” contains an extensive discussion on the use and application of dotplots. This was particularly helpful in devising an approach for presenting the information.
Reference1

Reference2

Reference3

Reference4

Software Program R
Software R version 10.1
*Required Libraries*
library(lattice)
library(SASxport)
library(vcd) # for color scheme
library(plotrix) # for pie chart
Graphical Parameters Setting up Graphical Parameters
hclcolors <- rainbow_hcl(4, l=50, start=50, c=75)[c(3,2,4,1)]
colorpalette <- c(hclcolors[1:3], 'grey45')

new.back <- trellis.par.get("background")
new.back$col <- "white"
newcol <- trellis.par.get("superpose.symbol")
newcol$col <- colorpalette
new.pan <- trellis.par.get("strip.background")
new.pan$col <- c('gray90','white')
trellis.par.set("background", new.back)
trellis.par.set("superpose.symbol", newcol)
trellis.par.set("strip.background",new.pan)

CodeExample1 - Attachment

CodeExample1 Figure 1 Code
advdat <- data.frame(REAS=c('Completed Study','Adverse Event',
'Lack of Efficacy', 'Withdrew Consent', 'Other'),
PCT=c(75, 7, 8, 6, 4))
rr <- rank(advdat$PCT)
mylayout <- layout(matrix(c(1,2,3,4), byrow=TRUE, ncol=4),
widths=c(1/9,2/9,1/3, 1/3),
heights=c(3/4,3/4,3/4,3/4))
par(mar=c(5,1,7,0))
plot(x=c(0,1), y=c(1,dim(advdat)[1]), axes=FALSE, ann=FALSE, type='n', xlab='',
ylab='')
text(x=1, y=1:dim(advdat)[1], advdat$REAS[rr], adj=c(1,NA), cex=.9,
col='black')
par(mar=c(5,0,7,1))
plot(x=advdat$PCT[rr], y=1:dim(advdat)[1], type='n', pch=16, axes=FALSE,
xlab='Percent')
abline(h=1:dim(advdat)[1], col='grey80')
box()
points(x=advdat$PCT[rr], y=1:dim(advdat)[1], pch=16, col='black')
axis(1)
mtext('Dotplot', 3, line=1, font=2)
#dotplot(reorder(REAS,PCT) ~ PCT, data=advdat)

plot(1:5,type="n",axes=FALSE)
box()
floating.pie(3,3,advdat$PCT, col=c('red','blue','green4','orange','violet'))
mtext('Pie Chart', 3, line=1, font=2)
legend(x=1, y=5, legend=advdat$REAS, fill=c('red','blue','green4','orange','violet'),
text.col=c('red','blue','green4','orange','violet'))

par(mar=c(5,2,7,1))
barplot(advdat$PCT[rr], names.arg=advdat$REAS[rr], horiz=TRUE, xlab='Percent')
mtext('Barplot', 3, line=1, font=2)

CodeExample2 - Attachment

CodeExample2 Figure 2 Code
dathigh <- subset(plotdat, TRT%in%'Xanomeline High Dose')

dotplot(PT ~ PCT, groups=TRT, data=dathigh,
xlab="Percent Reporting Event",
pch=16, col='black')
CodeExample3 - Attachment*

CodeExample3 Figure 3 Code
dotplot(reorder(PT,PCT) ~ PCT, groups=TRT, data=dathigh,
xlab="Percent Reporting Event",
pch=16, col='black')
CodeExample4 - Attachment*

CodeExample4 Figure 4 Code
dathigh$SOC <- c('GASTROINTESTINAL DISORDERS', 'PSYCHIATRIC DISORDERS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'CARDIAC DISORDERS','CARDIAC DISORDERS',
'MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS',
'SKIN AND SUBCUTANEOUS TISSUE DISORDERS',
'PSYCHIATRIC DISORDERS',
'RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS',
'GASTROINTESTINAL DISORDERS', 'NERVOUS SYSTEM DISORDERS',
'INVESTIGATIONS', 'SKIN AND SUBCUTANEOUS TISSUE DISORDERS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'NERVOUS SYSTEM DISORDERS', 'SKIN AND SUBCUTANEOUS TISSUE DISORDERS',
'CARDIAC DISORDERS', 'RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS',
'INFECTIONS AND INFESTATIONS', 'GASTROINTESTINAL DISORDERS',
'GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS',
'SKIN AND SUBCUTANEOUS TISSUE DISORDERS',
'SKIN AND SUBCUTANEOUS TISSUE DISORDERS','CARDIAC DISORDERS',
'SKIN AND SUBCUTANEOUS TISSUE DISORDERS','NERVOUS SYSTEM DISORDERS',
'NERVOUS SYSTEM DISORDERS',
'INFECTIONS AND INFESTATIONS','GASTROINTESTINAL DISORDERS')

sortmat <- function (Mat, Sort) {
m <- do.call("order", as.data.frame(Mat[, Sort]))
Mat[m, ]
} # Used to sort a matrix

dat2 <- sortmat(dathigh, c(4,3))
tt <- table(dat2$SOC)
plotcol <- rainbow(length(tt))
dat2$color <- rep(plotcol, tt)
mylayout <- layout(matrix(c(1,2,3), byrow=TRUE, ncol=3),
widths=c(2/7,3/7, 2/7))
par(mar=c(5,1,2,0))
plot(x=c(0,1), y=c(1,dim(dat2)[1]), axes=FALSE, ann=FALSE, type='n', xlab='', ylab='')
text(x=1, y=1:dim(dat2)[1], dat2$PT, adj=c(1,NA), cex=.9,
col=dat2$color)

par(mar=c(5,0,2,2))
plot(x=dat2$PCT, y=1:dim(dat2)[1], type='n', pch=16, axes=FALSE, xlab='Percent Reporting Event')
abline(h=1:dim(dat2)[1], col='grey80')
box()
points(x=dat2$PCT, y=1:dim(dat2)[1], pch=16, col=dat2$color)
axis(1)

par(mar=c(5,0,2,1))
plot(x=c(0,1), y=c(1,dim(dat2)[1]), axes=FALSE, ann=FALSE, type='n', xlab='', ylab='')
text(x=0, y=1:dim(dat2)[1], dat2$SOC, adj=c(0,NA), cex=.85,
col=dat2$color)

CodeExample5 - Attachment*

CodeExample5 Figure 5 Code
dotplot(reorder(PT,PCT) ~ PCT, groups=TRT, data=plotdat,
xlab="Percent Reporting Event",
pch=1:3,
key=list(
points=list(
col=trellis.par.get("superpose.symbol")$col[1:3],
pch=1:3),
text=list(
lab=levels(plotdat$TRT),
col=trellis.par.get('superpose.symbol')$col[1:3]),
columns=3, title='Treatment'))
CodeExample6 - Attachment*

CodeExample6 Figure 6 Code
# Simulate Data
set.seed(133)
notrelated <- NULL
for(i in 1:dim(plotdat)[1]){
notrelated[i] <- plotdat$PCT[i] - runif(1, 0, plotdat$PCT[i])
}
related <- plotdat$PCT - notrelated

plotdat2 <- data.frame(PT=rep(plotdat$PT, 2),
TRT=rep(plotdat$TRT, 2),
REL=factor(rep(c('Related','Not Related'), each=dim(plotdat)[1]),
levels=c('Related','Not Related')),
PCT=c(related, notrelated))

dotplot(reorder(PT,PCT) ~ PCT|REL, groups=TRT, data=plotdat2,
xlab="Percent Reporting Event",
pch=1:3,
key=list(
points=list(
col=trellis.par.get("superpose.symbol")$col[1:3],
pch=1:3),
text=list(
lab=levels(plotdat$TRT),
col=trellis.par.get('superpose.symbol')$col[1:3]),
columns=3, title='Treatment'))

R-Code - Attachment

R-Code

SAS-Code - Attachment

SAS-Code

Stata-Code - Attachment

Stata-Code

Other Code - Attachment

Other Code

Keywords

Disclaimer The opinions expressed in this document are those of the author and may not represent the opinions of the U.S. Food and Drug Administration or other authors.
Permission Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF ERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT OLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Reference Image Soukup_dotplot6_200.png
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Topic revision: r6 - 19 Jun 2012 - 13:41:53 - MaryBanach
 

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