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## Program to calculate effects for matched case-control studies
## Michael Hills
## Improved by BxC and MP
## Post Tartu 2007 version June 2007
effx.match<-function(response,
exposure,
match,
strata=NULL,
control=NULL,
base=1,
digits=3,
alpha=0.05,
data=NULL)
{
## stores the variable names for response, etc.
rname<-deparse(substitute(response))
ename<-deparse(substitute(exposure))
if (!missing(strata))sname<-deparse(substitute(strata))
## The control argument is more complex, as it may be a name or
## list of names
if(!missing(control)) {
control.arg <- substitute(control)
if (length(control.arg) > 1) {
control.names <- sapply(control.arg, deparse)[-1]
}
else {
control.names <- deparse(control.arg)
}
}
## If data argument is supplied, evaluate the arguments in that
## data frame.
if (!missing(data)) {
exposure <- eval(substitute(exposure), data)
response <- eval(substitute(response), data)
match <- eval(substitute(match),data)
if (!missing(strata)) {
strata <- eval(substitute(strata), data)
}
if (!missing(control))
control <- eval(substitute(control), data)
}
## performs a few other checks
if(rname==ename)stop("Same variable specified as response and exposure")
if (!missing(strata)) {
if(rname==sname)stop("Same variable specified as response and strata")
if(sname==ename)stop("Same variable specified as strata and exposure")
}
if(!is.numeric(response))stop("Response must be numeric, not a factor")
if(!missing(strata)&!is.factor(strata))stop("Stratifying
variable must be a factor")
tmp<-(response==0 | response==1)
if(all(tmp,na.rm=TRUE)==FALSE)
stop("Binary response must be coded 0,1 or NA")
if(class(exposure)[1]=="ordered") {
exposure<-factor(exposure, ordered=F)
}
## Fix up the control argument as a named list
if (!missing(control)) {
if (is.list(control)) {
names(control) <- control.names
}
else {
control <- list(control)
names(control) <- control.names
}
}
## prints out some information about variables
cat("---------------------------------------------------------------------------","\n")
cat("response : ", rname, "\n")
cat("exposure : ", ename, "\n")
if(!missing(control))cat("control vars : ",names(control),"\n")
if(!missing(strata)) cat("stratified by : ",sname,"\n")
cat("\n")
if(is.factor(exposure)) {
cat(ename,"is a factor with levels: ")
cat(paste(levels(exposure),collapse=" / "),"\n")
cat( "baseline is ", levels( exposure )[base] ,"\n")
exposure <- Relevel( exposure, base )
}
else {
cat(ename,"is numeric","\n")
}
if(!missing(strata)) {
cat(sname,"is a factor with levels: ")
cat(paste(levels(strata),collapse="/"),"\n")
}
cat("effects are measured as odds ratios","\n")
cat("---------------------------------------------------------------------------","\n")
cat("\n")
## gets number of levels for exposure if a factor
if(is.factor(exposure)) {
nlevE<-length(levels(exposure))
}
else {
nlevE<-1
}
## labels the output
if(is.factor(exposure)) {
cat("effect of",ename,"on",rname,"\n")
}
else {
cat("effect of an increase of 1 unit in",ename,"on",rname,"\n")
}
if(!missing(control)) {
cat("controlled for",names(control),"\n\n")
}
if(!missing(strata)) {
cat("stratified by",sname,"\n\n")
}
## no stratifying variable
if(missing(strata)) {
if(missing(control)) {
m<-clogit(response~exposure+strata(match))
cat("number of observations ",m$n,"\n\n")
}
else {
m<-clogit(response~.+exposure+strata(match),
subset=!is.na(exposure),data=control)
cat("number of observations ",m$n,"\n\n")
mm<-clogit(response~.+strata(match),
subset=!is.na(exposure),data=control)
}
res<-ci.lin(m,subset=c("exposure"),Exp=TRUE,alpha=alpha)[,c(5,6,7)]
res<-signif(res,digits)
if(nlevE<3) {
names(res)[1]<-c("Effect")
}
else {
colnames(res)[1]<-c("Effect")
if(is.factor(exposure)) {
ln <- levels(exposure)
rownames(res)[1:nlevE-1]<-paste(ln[2:nlevE],"vs",ln[1])
}
}
print(res)
if(missing(control)) {
chisq<-round(summary(m)$logtest[1],2)
df<-round(summary(m)$logtest[2])
p<-round(summary(m)$logtest[3],3)
cat("\n")
cat("Test for no effects of exposure: ","\n")
cat("chisq=",chisq, " df=",df, " p-value=",format.pval(p,digits=3),"\n")
invisible(list(res,paste("Test for no effects of exposure on",
df,"df:","p=",format.pval(p,digits=3))))
}
else {
aov <- anova(mm,m,test="Chisq")
cat("\nTest for no effects of exposure on",
aov[2,3],"df:",
"p-value=",format.pval(aov[2,5],digits=3),"\n")
invisible(list(res,paste("Test for no effects of exposure on",
aov[2,3],"df:","p=",format.pval(aov[2,5],digits=3))))
}
}
## stratifying variable
if(!missing(strata)) {
sn <- levels(strata)
nlevS<-length(levels(strata))
if(missing(control)) {
m<-clogit(response~strata/exposure+strata(match))
cat("number of observations ",m$n,"\n\n")
mm<-clogit(response~strata+exposure+strata(match))
}
else {
m <-clogit(response~strata/exposure + . +strata(match),
data=control)
cat("number of observations ",m$n,"\n\n")
mm <-clogit(response~strata+exposure + . +strata(match),
data=control)
}
res<-ci.lin(m,Exp=TRUE,alpha=alpha,subset="strata")[c(-1:-(nlevS-1)),c(5,6,7)]
res<-signif(res,digits)
colnames(res)[1]<-c("Effect")
if(is.factor(exposure)) {
ln<-levels(exposure)
newrownames<-NULL
for(i in c(1:(nlevE-1))) {
newrownames<-c(newrownames,
paste("strata",sn[1:nlevS],"level",ln[i+1],"vs",ln[1]))
}
}
else {
newrownames<-paste("strata",sn[1:nlevS])
}
rownames(res)<-newrownames
aov<-anova(mm,m,test="Chisq")
print( res )
cat("\nTest for effect modification on",
aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3),"\n")
invisible(list(res,paste("Test for effect modification on",
aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3))))
}
}
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