cosmesis {MLEcens} | R Documentation |
Breast cosmesis data
Description
An example data set with univariate interval censored data (and one covariate). The data come from a retrospective study that compared the cosmetic effect of two types of treatments for early breast cancer patients: radiotherapy alone, and radiotherapy with adjuvant chemotherapy. The time of interest was the time to breast retraction (in months).
Usage
data(cosmesis)
Format
A matrix containing 94 rows and 3 columns. Each row
(x1,x2,tr) corresponds to a subject in the study.
The interval (x1,x2]
contains the unobservable time of breast retraction (in months).
The variable tr
indicates the treatment: tr=0
for patients who were treated
with radiotherapy alone (RT alone), and tr=1
for patients
who were treated with radiotherapy and chemotherapy (RT+CT).
We use the value 100 to represent infinity.
Source
Finkelstein and Wolfe (1985). A semiparametric model for regression analysis of interval-censored failure time data. Biometrics 41 933-945.
Examples
data(cosmesis)
# Split data according to treatment group
cosmesis0 <- cosmesis[cosmesis[,3]==0, 1:2]
cosmesis1 <- cosmesis[cosmesis[,3]==1, 1:2]
n0 <- nrow(cosmesis0)
n1 <- nrow(cosmesis1)
# Add dummy y-intervals (0,1)
cosmesis0 <- cbind(cosmesis0, rep(0,times=n0), rep(1,times=n0))
cosmesis1 <- cbind(cosmesis1, rep(0,times=n1), rep(1,times=n1))
# Compute MLEs in both treatment groups
mle0 <- computeMLE(cosmesis0)
mle1 <- computeMLE(cosmesis1)
# Plot MLEs
par(mfrow=c(2,2))
# Density for women who were treated with radio theorapy alone
plotDens1(mle0, margin=1, col="black", main="Density for time to breast
retraction (RT alone)", xlab="time (months)", ylab="density")
# Density for women who were treated with radio therapy + chemo therapy
plotDens1(mle1, margin=1, col="red", main="Density for time to breast
retraction (RT+CT)", xlab="time (months)", ylab="density")
# Survival functions for both groups, plus legend
plotCDF1(mle0, margin=1, surv=TRUE, col="black",
main="Survival functions", xlab="time (months)", ylab="probability")
plotCDF1(mle1, margin=1, surv=TRUE, col="red", add=TRUE)
legend(3,.3,c("RT alone","RT+CT"), lty=1, col=c("black","red"))