# ae.dotplot

##### AE (Adverse Events) dotplot of incidence and relative risk

A two-panel display of the most frequently occurring AEs in the active arm of a clinical study. The first panel displays their incidence by treatment group, with different symbols for each group. The second panel displays the relative risk of an event on the active arm relative to the placebo arm, with 95% confidence intervals for a 2$\times$2 table. The AEs are ordered by relative risk so that events with the largest increases in risk for the active treatment are prominent at the top of the display.

##### Usage

```
ae.dotplot(xr,
A.name = levels(xr$RAND)[1], B.name = levels(xr$RAND)[2],
col.AB = c("red","blue"), pch.AB = c(16, 17),
main.title = "Most Frequent On-Therapy Adverse Events Sorted by Relative Risk",
main.cex = 1,
cex.AB.points = NULL, cex.AB.y.scale = 0.6,
position.left = c(0, 0, 0.7, 1), position.right = c(0.61, 0, 0.98, 1),
key.y = -0.2, CI.percent=95)
logrelrisk(ae, A.name, B.name, crit.value=1.96)
panel.ae.leftplot(x, y, groups, col.AB, ...)
panel.ae.rightplot(x, y, ..., lwd=6, lower, upper)
panel.ae.dotplot(x, y, groups, ..., col.AB, pch.AB, lower, upper) ## R only
```

##### Arguments

- ae
- data.frame containing the first 4 columns of
`xr`

described below. - xr
`RAND`

: treatment as randomized (factor).\`PREF`

: adverse event symptom name (factor).\`SN`

: number of patients in treatment group.\`SAE`

: number of patients in each group for whom the ev- A.name, B.name
- Names of treatment groups (in
`x$RAND`

). - col.AB, pch.AB, cex.AB.points
- color, plotting character and character expansion for the individual points on the left plot.
- cex.AB.y.scale
- Character expansion for the left tick labels (the symptom names).
- main.title, main.cex
- Main title and character expansion for the
combined plot in
`ae.dotplot`

. - position.left, position.right
`position`

of the left and right plots. This argument is use in S-Plus only, not in R. See the discussion of`position`

in`print.trellis`

in R,- key.y
- Position of the key (legend) in the combined plot. This
is the
`y`

argument of the`key`

. See`key`

in S-Plus, and the discussion of the`key`

argument to - crit.value
- Critical value used to compute confidence intervals
on the log relative risk. Defaults to 1.96. User is responsible
for specifying both
`crit.value`

and`CI.percent`

consistently. - CI.percent
- Confidence percent associated with the
`crit.value`

Defaults to 95. User is responsible for specifying both`crit.value`

and`CI.percent`

consistently. - x, y, groups, lwd, ...
- standard panel function arguments.
- lower, upper

##### Details

The second panel shows relative risk of an event on the active arm (treatment B) relative to the placebo arm (treatment A), with 95% confidence intervals for a 2$\times$2 table. Confidence intervals on the log relative risk are calculated using the asymptotic standard error formula given as Equation 3.18 in Agresti A., Categorical Data Analysis. Wiley: New York, 1990.

##### Value

`logrelrisk`

takes an input data.frame of the form`x`

described in the argument list and returns a data.frame consisting of the input argument with additional columns as described in the argument`xr`

. The result column of symptom names`PREF`

is an ordered factor, with the order specified by the relative risk.`ae.leftplot`

returns a`"trellis"`

object containing a horizontal dotplot of the percents against each of the symptom names.`ae.rightplot`

returns a`"trellis"`

object containing a horizontal plot on the log scale of the relative risk confidence intervals against each of the symptom names.`ae.dotplot`

calls both`ae.leftplot`

and`ae.rightplot`

and combines their plots into a single display with a single set of left axis labels, a main title, and a key. The value returned invisibly is a list of the full left trellis object and the right trellis obect with its left labels blanked out. Printing the value will not usually be interesting as the main title and key are not included. It is better to call`ae.dotplot`

directly, perhaps with a change in some of the positioning arguments.

##### References

Ohad Amit, Lane, Peter W., & Richard M. Heiberger (2007).
Graphical Approaches to the Analysis of Safety Data from Clinical
Trials. Pharmaceutical Statistics.

##### Examples

```
## variable names in the input data.frame aeanonym
## RAND treatment as randomized
## PREF adverse event symptom name
## SN number of patients in treatment group
## SAE number of patients in each group for whom the event PREF was observed
##
## Input sort order is PREF/RAND
aeanonym <- read.table(hh("datasets/aedotplot.dat"), header=TRUE, sep=",")
aeanonym[1:4,]
## Calculate log relative risk and confidence intervals (95% by default).
## logrelrisk sets the sort order for PREF to match the relative risk.
aeanonymr <- logrelrisk(aeanonym,
A.name=levels(aeanonym$RAND)[1],
B.name=levels(aeanonym$RAND)[2])
aeanonymr[1:4,]
## construct and print plot on current graphics device
ae.dotplot(aeanonymr,
A.name="TREATMENT A (N=216)",
B.name="TREATMENT B (N=431)")
## export.eps(h2("stdt/figure/aerelrisk.eps"))
## This looks great on screen and exports badly to eps.
## We recommend drawing this plot directly to the postscript device:
##
## trellis.device(postscript, color=TRUE, horizontal=TRUE,
## colors=ps.colors.rgb[
## c("black", "blue", "red", "green",
## "yellow", "cyan","magenta","brown"),],
## onefile=FALSE, print.it=FALSE,
## file=h2("stdt/figure/aerelrisk.ps"))
## ae.dotplot(aeanonymr,
## A.name="TREATMENT A (N=216)",
## B.name="TREATMENT B (N=431)")
## dev.off()
```

*Documentation reproduced from package HH, version 2.1-15, License: GPL (>= 2)*