Nothing
## Program to calculate effects
## Michael Hills
## Improved by Bendix Carstensen and Martyn Plummer
## Post Tartu 2007 version June 2007
## Addition allowing a TRUE/FALSE as binary outcome
## and possibility or relative risk for binary outcomes and rate
## difference for failure outcomes, BxC, Ocitober 2012
effx<-function(response,
type="metric",
fup=NULL,
exposure,
strata=NULL,
control=NULL,
weights=NULL,
eff=NULL,
alpha=0.05,
base=1,
digits=3,
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)
}
## Match the type argument
type <- match.arg(type, c("metric", "failure", "count", "binary"))
## Check for missing arguments
if (missing(response))
stop("Must specify the response","\n")
if (missing(exposure))
stop("Must specify the exposure","\n")
if (type == "failure" && missing(fup)) {
stop("Must specify a follow-up variable when type is failure")
}
## 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 data argument is supplied, evaluate the arguments in that
## data frame.
if (!missing(data)) {
exposure <- eval(substitute(exposure), data)
response <- eval(substitute(response), data)
if (!missing(strata))
strata <- eval(substitute(strata), data)
if (!missing(control))
control <- eval(substitute(control), data)
if (!missing(fup))
fup <- eval(substitute(fup), data)
if (!missing(weights)) {
weights <- eval(substitute(weights), data)
}
}
## Now check validity of evaluated arguments
if(is.logical(response)) response <- as.numeric(response)
if(!is.numeric(response))
stop("Response must be numeric, not a factor")
if (!missing(weights) && type != "binary") {
stop("weights only allowed for a binary response")
}
if (!missing(strata) && !is.factor(strata))
stop("Stratifying variable must be a factor")
if(type=="binary") {
if( is.null(eff) ) eff<-"OR"
if( !(eff %in% c("OR","RR") ) ) stop( "Only RR and OR allowed for binary response" )
response <- as.numeric(response)
tmp<-(response==0 | response==1)
if(all(tmp,na.rm=TRUE)==FALSE)
stop("Binary response must be logical or coded 0,1 or NA")
}
# If a count is given we are actually using the fup
if(type=="count") fup<-is.na(response)*1
if(type=="failure") {
if( is.null(eff) ) eff<-"RR"
response <- as.numeric(response)
tmp<-(response==0 | response==1)
if(all(tmp,na.rm=TRUE)==FALSE)
stop("Failure response must be logical or coded 0,1 or NA")
}
## If exposure is an ordered factor, drops the order.
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)) {
control.names <- sapply(control.arg, deparse)
if (control.names[1] == "list" &&
length(control.names) == length(control) + 1)
{
control.names <- control.names[-1]
}
else {
control.names <-
paste0(deparse(control.arg), "[", 1:length(control), "]")
}
names(control) <- control.names
}
else {
control <- list(control)
names(control) <- deparse(control.arg)
}
}
## prints out some information about variables
cat("---------------------------------------------------------------------------","\n")
cat("response : ", rname, "\n")
cat("type : ", type, "\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")
exposure <- Relevel( exposure, base )
cat( "baseline is ", levels( exposure )[1] ,"\n")
}
else {
cat(ename,"is numeric","\n")
}
if(!missing(strata)) {
cat(sname,"is a factor with levels: ")
cat(paste(levels(strata),collapse="/"),"\n")
}
if(type=="metric")cat("effects are measured as differences in means","\n")
if(type=="binary")
{
if( eff=="OR" | is.null(eff))cat("effects are measured as odds ratios","\n")
if( eff=="RR" )cat("effects are measured as relative risk","\n")
}
if(type=="failure")
{
if( eff=="RR" | is.null(eff))cat("effects are measured as rate ratios","\n")
if( eff=="RD" )cat("effects are measured as rate differences","\n")
}
cat("---------------------------------------------------------------------------","\n")
cat("\n")
## translates type of response and choice of eff into family
if ( type=="metric") family<-gaussian
if ( type=="binary") family<-binomial(link=logit)
if ( type=="failure" | type=="count") family<-poisson(link=log)
if ( !is.null(eff) )
{
if (type=="binary" & eff=="RR") family<-binomial(link=log)
if (type=="failure" & eff=="RD") family<-poisson(link=identity)
}
## gets number of levels for exposure if a factor
if(is.factor(exposure)) {
nlevE<-length(levels(exposure))
}
## 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(type=="metric") {
if(missing(control)) {
m<-glm(response~exposure,family=family)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~1,family=family,subset=!is.na(exposure))
}
else {
m<-glm(response~.+exposure,family=family,
subset=!is.na(exposure),data=control)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~.,family=family,
subset=!is.na(exposure),data=control)
}
res<-ci.lin(m,subset=c("Intercept","exposure"),alpha=alpha)
res<-res[,c(1,5,6)]
}
if(type=="binary") {
if(missing(control)) {
m<-glm(response~exposure,family=family,weights=weights)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~1,family=family,subset=!is.na(exposure),weights=weights)
}
else {
m<-glm(response~.+exposure,family=family,
subset=!is.na(exposure),data=control,weights=weights)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~.,family=family,
subset=!is.na(exposure),data=control,weights=weights)
}
res<-ci.lin(m,subset=c("Intercept","exposure"),Exp=TRUE,alpha=alpha)
res<-res[,c(5,6,7)]
}
if (type=="failure" | type=="count") {
if (missing(control)) {
m<-glm(response/fup~exposure,weights=fup,family=family)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response/fup~1,weights=fup,family=family,
subset=!is.na(exposure))
}
else {
m<-glm(response/fup~.+exposure,weights=fup,family=family, data=control)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response/fup~.,weights=fup,family=family,
subset=!is.na(exposure),data=control)
}
res<-ci.exp(m,subset=c("Intercept","exposure"),alpha=alpha,Exp=(eff=="RR"))
}
res<-signif(res,digits)
colnames(res)[1]<-c("Effect")
if(is.factor(exposure)) {
ln <- levels(exposure)
rownames(res)[2:nlevE]<-paste(ln[2:nlevE],"vs",ln[1])
}
aov <- anova(mm,m,test="Chisq")
print( res[-1,] )
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-value=",format.pval(aov[2,5],digits=3))))
}
## stratifying variable
if(!missing(strata)) {
sn <- levels(strata)
nlevS<-length(levels(strata))
if(type=="metric") {
if(missing(control)) {
m<-glm(response~strata/exposure,family=family)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~strata+exposure,family=family)
}
else {
m <-glm(response~strata/exposure + .,family=family,
data=control)
cat("number of observations ",m$df.null+1,"\n\n")
mm <-glm(response~strata+exposure + .,family=family,
data=control)
}
res<-ci.lin(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),c(1,5,6)]
}
if(type=="binary") {
if(missing(control)) {
m<-glm(response~strata/exposure,family=family,weights=weights)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~strata+exposure,family=family,weights=weights)
}
else {
m <-glm(response~strata/exposure + .,family=family,
data=control,weights=weights)
cat("number of observations ",m$df.null+1,"\n\n")
mm <-glm(response~strata+exposure + .,family=family,
data=control,weights=weights)
}
res<-ci.exp(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),]
}
if (type=="failure" | type=="count") {
if(missing(control)) {
m<-glm(response/fup~strata/exposure,weights=fup,family=family)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response~strata+exposure,weights=fup,family=family)
}
else {
m <-glm(response/fup~.+strata/exposure,weights=fup,family=family,data=control)
cat("number of observations ",m$df.null+1,"\n\n")
mm<-glm(response/fup~.+strata+exposure,weights=fup,family=family,data=control)
}
res<-ci.exp(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),]
}
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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