gllm (version 0.38)

hildesheim: Invasive Cervical Cancer v exposure to Herpes Simplex Virus

Description

The case-control study of Hildesheim et al (1991) has been reanalysed by several authors (Carroll et al 1993; Spiegelhalter et al 1999; Prescott et al 2002). Exposure to Herpes Simplex Virus in cases suffering from invasive cervical cancer and in unaffected controls was assessed by Western Blot in all cases and controls and by a gold-standard refined Western blotting in a subset of 115 subjects.

Usage

data(hildesheim)

Format

A data frame table.

References

Carroll NJ, Gail MH, Lubin JH (1993) Case-control studies with errors in covariates. J Am Statist Assoc 88, 185-199.

Prescott GJ, Garthwaite PH (2002) A simple bayesian analysis of misclassified binary data with a validation substudy. Biometrics 58, 454-458.

Spiegelhalter DJ, Thomas A, Best NG (1999) Win-Bugs, Version 1.2. Technical Report. Cambridge: UK.

Examples

data(hildesheim)
ftable(xtabs(Freq ~ case+HSV.inac+HSV.gold, hildesheim))
fisher.test(xtabs(Freq ~ case+HSV.inac, hildesheim))
fisher.test(xtabs(Freq ~ case+HSV.gold, hildesheim, subset=HSV.gold!="?"))

#
# Combined analysis (ordered as incomplete then complete data)
#
y<-hildesheim$Freq[c(3,9,6,12,1,2,7,8,4,5,10,11)]
#
# Map observed table onto underlying 2x2x2x2 table
#
s <-c(1, 1, 2, 2, 3, 3, 4, 4,
      5, 6, 7, 8, 9, 10, 11, 12)
#
substudy  <- 2-as.integer(gl(2,8,16))
hsv.inac  <- 2-as.integer(gl(2,4,16))
hsv.gold  <- 2-as.integer(gl(2,2,16))
cancer    <- 2-as.integer(gl(2,1,16))

require(gllm)
res<-gllm(y,s, ~substudy+hsv.inac*hsv.gold*cancer)
print(summary.gllm(res))
#
# Bootstrap the collapsed table to get estimated OR for reliable measures
#
# a and b are binary vectors the length of the *full* table
# and define the variables for which the odds ratio is to be
# estimated, here the reliable measure of HSV exposure and Ca Cx
#
boot.hildesheim <- function (y,s,X,nrep,a,b) {
  z<-boot.gllm(y,s,X,R=nrep)
  boot.tab<-cbind(apply(z[,a & b],1,sum),
                  apply(z[,!a & b],1,sum),
                  apply(z[,a & !b],1,sum),
                  apply(z[,!a & !b],1,sum))
  oddsr<-boot.tab[,1]*boot.tab[,4]/boot.tab[,2]/boot.tab[,3] 
  hildesheim.tab<-data.frame( c("yes","yes","no","no"),
                              c("yes","no","yes","no"),
                              boot.tab[1,],
                              apply(boot.tab[2:(1+nrep),],2,sd))
  colnames(hildesheim.tab)<-c("Precise HSV","Cervical Cancer",
                            "Estimated Count","Bootstrap S.E.")
  print(hildesheim.tab)
  cat("\nEstimated OR=",oddsr[1],"\n")
  cat("        Bias=",oddsr[1]-mean(oddsr[2:(1+nrep)]),"\n")
  cat("Bootstrap SE=",sd(oddsr[2:(1+nrep)]),"\n\nQuantiles\n\n")
  print(quantile(oddsr[2:(1+nrep)],c(0.025,0.50,0.975)))

  b<-mean(log(oddsr[2:(1+nrep)]))
  se<-sd(log(oddsr[2:(1+nrep)]))
  ztest<-b/se
  cat("\n      Estimated log(OR)=",log(oddsr[1]),"\n",
      "Bootstrap mean log(OR)=",b,"\n",
      "          Bootstrap SE=",se,"\n",
      "                Wald Z=",ztest," (P=",2*pnorm(ztest,lower=FALSE),")\n")
}
boot.hildesheim(y,s,~substudy+hsv.inac*hsv.gold*cancer,nrep=50,cancer,hsv.gold)