pbc: Mayo Clinic Primary Biliary Cirrhosis Data
In SMPracticals: Practicals for Use with Davison (2003) Statistical Models

pbc	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")

SMPracticals documentation built on May 29, 2024, 12:19 p.m.