pbc {SMPracticals}R Documentation

Mayo Clinic Primary Biliary Cirrhosis Data

Description

Followup of 312 randomised and 108 unrandomised patients with primary biliary cirrhosis, a rare autoimmune liver disease, at Mayo Clinic.

Usage

data(pbc)

Format

A data frame with 418 observations on the following 20 variables.

age
in years
alb
serum albumin
alkphos
alkaline phosphotase
ascites
presence of ascites
bili
serum bilirubin
chol
serum cholesterol
edema
presence of edema
edtrt
0 no edema, 0.5 untreated or successfully treated 1 unsuccessfully treated edema
hepmeg
enlarged liver
time
survival time
platelet
platelet count
protime
standardised blood clotting time
sex
1=male
sgot
liver enzyme (now called AST)
spiders
blood vessel malformations in the skin
stage
histologic stage of disease (needs biopsy)
status
censoring status
trt
1/2/-9 for control, treatment, not randomised
trig
triglycerides
copper
urine copper

Source

Fleming, T. R. and Harrington, D. P. (1991) Counting Processes and Survival Analysis. Wiley: New York.

References

Davison, A. C. (2003) Statistical Models. Cambridge University Press. Page 549.

Examples

data(pbc)  
# to make version of dataset used in book
pbcm <- pbc[(pbc$trt!=-9),]
pbcm$copper[(pbcm$copper==-9)] <- median(pbcm$copper[(pbcm$copper!=-9)])
pbcm$platelet[(pbcm$platelet==-9)] <- median(pbcm$platelet[(pbcm$platelet!=-9)])
attach(pbcm)

library(survival)
par(mfrow=c(1,2),pty="s")
plot(survfit(Surv(time,status)~trt),ylim=c(0,1),lty=c(1,2),
   ylab="Survival probability",xlab="Time (days)")
plot(survfit(coxph(Surv(time,status)~trt+strata(sex))),ylim=c(0,1),lty=c(1,2),
   ylab="Survival probability",xlab="Time (days)")
lines(survfit(coxph(Surv(time,status)~trt)),lwd=2)
# proportional hazards model fit
fit <- coxph(formula = Surv(time, status) ~ age + alb + alkphos + ascites + 
      bili + edtrt + hepmeg + platelet + protime + sex + spiders, data=pbcm)
summary(fit)
step.fit <- step(fit,direction="backward")

[Package SMPracticals version 1.3 Index]