lung.saemix: NCCTG Lung Cancer Data, in SAEM format

lung.saemixR Documentation

NCCTG Lung Cancer Data, in SAEM format

Description

The lung.saemix contains survival data in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities. This data is available in the survival library for R and has been reformatted here for use in saemix (see details).

Usage

lung.saemix

Format

This data frame contains the following columns:

id

subject index in file

inst

institution code

time

observation time since the beginning of follow-up

status

0=alive, 1=dead

cens

0=observed, 1=censored

sex

patient gender (0=male, 1=female)

age

age in years

ph.ecog

ECOG performance score as rated by the physician. 0=asymptomatic, 1= symptomatic but completely ambulatory, 2= in bed <50% of the day, 3= in bed > 50% of the day but not bedbound, 4 = bedbound

ph.karno

Karnofsky performance score (bad=0-good=100) rated by physician (%)

pat.karno

Karnofsky performance score (bad=0-good=100) rated by patient (%)

meal.cal

calories consumed at meals (cal)

wt.loss

weight loss in last six months (pounds)

Details

The data in the lung.saemix was reformatted from the lung cancer dataset (see data(cancer, package="survival")). Patients with missing age, sex, institution or physician assessments were removed from the dataset. Status was recoded as 1 for death and 0 for a censored event, and a censoring column was added to denote whether the patient was dead or alive at the time of the last observation. For saemix, a line at time=0 was added for all subjects. Finally, subjects were numbered consecutively from 0 to 1.

Source

Terry Therneau from the survival package in R

References

CL Loprinzi, JA Laurie, HS Wieand, JE Krook, PJ Novotny, JW Kugler, et al. (1994). Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. Journal of Clinical Oncology. 12(3):601-7.

Examples

data(lung.saemix)

saemix.data<-saemixData(name.data=lung.saemix,header=TRUE,name.group=c("id"),
name.predictors=c("time","status","cens"),name.response=c("status"),
name.covariates=c("age", "sex", "ph.ecog", "ph.karno", "pat.karno", "wt.loss","meal.cal"),
units=list(x="days",y="",covariates=c("yr","","-","%","%","cal","pounds")))
weibulltte.model<-function(psi,id,xidep) {
  T<-xidep[,1]
  y<-xidep[,2] # events (1=event, 0=no event)
  cens<-which(xidep[,3]==1) # censoring times (subject specific)
  init <- which(T==0)
  lambda <- psi[id,1] # Parameters of the Weibull model
  beta <- psi[id,2]
  Nj <- length(T)
  ind <- setdiff(1:Nj, append(init,cens)) # indices of events
  hazard <- (beta/lambda)*(T/lambda)^(beta-1) # H'
  H <- (T/lambda)^beta # H
  logpdf <- rep(0,Nj) # ln(l(T=0))=0
  logpdf[cens] <- -H[cens] + H[cens-1] # ln(l(T=censoring time))
  logpdf[ind] <- -H[ind] + H[ind-1] + log(hazard[ind]) # ln(l(T=event time))
  return(logpdf)
}
saemix.model<-saemixModel(model=weibulltte.model,description="time model",modeltype="likelihood",
                psi0=matrix(c(1,2),ncol=2,byrow=TRUE,dimnames=list(NULL,  c("lambda","beta"))),
                transform.par=c(1,1),covariance.model=matrix(c(1,0,0,0),ncol=2, byrow=TRUE))
saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE, displayProgress=FALSE)

tte.fit<-saemix(saemix.model,saemix.data,saemix.options)

# The fit from saemix using the above Weibull model may be compared 
# to the non-parametric KM estimate
## Not run: 
library(survival)
  lung.surv<-lung.saemix[lung.saemix$time>0,]
  lung.surv$status<-lung.surv$status+1
  Surv(lung.surv$time, lung.surv$status) # 1=censored, 2=dead
  f1 <- survfit(Surv(time, status) ~ 1, data = lung.surv)
  xtim<-seq(0,max(lung.saemix$time), length.out=200)
  estpar<-tte.fit@results@fixed.effects
  ypred<-exp(-(xtim/estpar[1])^(estpar[2]))
  plot(f1, xlab = "Days", ylab = "Overall survival probability")
  lines(xtim,ypred, col="red",lwd=2)

## End(Not run)

saemix documentation built on Aug. 5, 2022, 5:25 p.m.

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