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Title: Time to AE Occurrence

ClinAEStatGraphicsForm edit

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Title Time to AE Occurrence
Graph_Subgroup Adverse Events
AE_Clinical_Question 3. Is there a difference in the time to the first event across treatment groups?, 4. What are the trends of time to the first event among different AEs
Description This plot shows the probability of occurrence of adverse events over time by treatment group. Each color indicates a different treatment and each panel a different AE.
Contributor/Email Mat Soukup (email: Mat.Soukup@fda.hhs.gov)
Max Cherny (Max.2.Cherny@gsk.com)
Background

Keywords

References

Datasets Other
CDISC_Data

Other_Data Time to Event Data
CLASSIFICATIONS

Graph_Type Survival Plot
Variable_Relationship Survival
Data_Types Time to Event
Special_Considerations

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

R-Code /*-------------------------------------------------------------------*/
/*--Forest Plot for CTSPedia --*/
/*--R Code --*/
/*--Name: Mat Soukup (modified by Max Cherny --*/
/*--Company: GSK --*/
/*--Date : 30JUL 2012 --*/
/*-------------------------------------------------------------------*/
rm(list = ls()) # clear objects
graphics.off() # close graphics windows

##### Time to Event Graphic
library(lattice)
library(vcd)
library(survival)
set.seed(126)
times <- abs(c(rnorm(200, 5, 10), rnorm(200, 9, 10), rnorm(200, 13, 10), rnorm(200, 20, 10),
rnorm(200, 7, 10), rnorm(200, 11, 10), rnorm(200, 15, 10), rnorm(200, 24, 10),
rnorm(200, 13, 10), rnorm(200, 18, 10), rnorm(200, 23, 10), rnorm(200, 28, 10),
rnorm(200, 16, 10), rnorm(200, 21, 10), rnorm(200, 26, 10), rnorm(200, 31, 10)))
times[times < 0] <- 3
treat <- rep(rep(c('High','Mid','Low','Placebo'),each=200),4)
term <- rep(c('Headache','Dizziness','Nausea','Abdominal Pain'), each=800)
bin1h <- c(rep(0, 40),rep(1,60))
bin1m <- c(rep(0, 45),rep(1,55))
bin1l <- c(rep(0, 50),rep(1,50))
bin1p <- c(rep(0, 65),rep(1,35))
bin2h <- c(rep(0, 65),rep(1,35))
bin2m <- c(rep(0, 70),rep(1,30))
bin2l <- c(rep(0, 80),rep(1,20))
bin2p <- c(rep(0, 90),rep(1,10))
cens <- c(sample(bin1h, 200, replace=TRUE), sample(bin1m, 200, replace=TRUE),
sample(bin1l, 200, replace=TRUE), sample(bin1p, 200, replace=TRUE),
sample(bin2h, 200, replace=TRUE), sample(bin2m, 200, replace=TRUE),
sample(bin2l, 200, replace=TRUE), sample(bin2p, 200, replace=TRUE),
sample(bin1h, 200, replace=TRUE), sample(bin1m, 200, replace=TRUE),
sample(bin1l, 200, replace=TRUE), sample(bin1p, 200, replace=TRUE),
sample(bin2h, 200, replace=TRUE), sample(bin2m, 200, replace=TRUE),
sample(bin2l, 200, replace=TRUE), sample(bin2p, 200, replace=TRUE))
dat <- data.frame(times, treat, term, cens)
aes <- c('Headache','Dizziness','Nausea','Abdominal Pain')
survlist <- NULL
timelist <- NULL
trtlist <- NULL
for(j in 1:4){
aedat <- dat[dat$term%in%aes[1],]
nd <- length(unique(aedat$id))
aest <- NULL

# Run a Cox model
fit <- survfit(Surv(times,cens)~treat, data=aedat)
#Store output for plot

trtlistj? <- list(rep(names(fit$strata),fit$strata))
survlistj? <- list(fit$surv)
timelistj? <- list(fit$time)
}
# Create one big data set for plotting
nae <- c(800, 800, 800, 800)
aelabel <- c('Headache','Dizziness','Nausea','Abdominal Pain')
aelist <- NULL
for(k in 1:4){
aelistk? <- rep(aelabel[k],times=nae[k])
}
panarg <- unlist(aelist)
trtarg <- unlist(trtlist)
survarg <- unlist(survlist)
timearg <- unlist(timelist)
library(vcd)
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[c(3,1,2,4)]
newcol$pch <- c(16,1,4,8)
new.line <- trellis.par.get('superpose.line')
new.line$col <- colorpalette[c(3,1,2,4)]
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("superpose.line", new.line)
trellis.par.set("strip.background",new.pan)
lines_style=c(1,2,3,6)
lines_widths=c(6,4,3,1)
xyplot((1-survarg)~timearg|panarg, groups=trtarg,
type='s',

scales=list(
x=list(alternating=c(1, 1)),
y=list(alternating=c(1, 1))
) ,

lwd=lines_widths,
lty = lines_style,
xlab='Time (days)',
ylab='Proportion of Subjects Reporting Event',
col=trellis.par.get("superpose.symbol")$col[1:4],
key=list(lines=list(lty=lines_style, lwd=lines_widths,




#the numbers for N are just dummy numbers. In the actual program they would need to be calculated
col=trellis.par.get("superpose.symbol")$col[1:4]),


text=list(c('High (N=100)','Mid (N=110)','Low (N=94)','Placebo (N=130)'),
col=trellis.par.get("superpose.symbol")$col[1:4]),
columns=4, title='Treatment', between=.6))
SAS-Code - Attachment Cherny - Time to Event - SAS Code
SAS-Code

Stata-Code - Attachment

Stata-Code

Other Code - Attachment

Other Code

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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:

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Reference Image Cherny_time_event_200.png
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Topic revision: r5 - 01 May 2013 - 17:50:00 - MaryBanach
 

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