Nothing
get.score.main <-
function(time,event,treat,bio,covar=NULL,nfolds=5,alpha=0.5) {
# check input data
stopifnot(length(time)==length(event))
stopifnot(length(time)==length(treat))
stopifnot(!missing(bio))
stopifnot(treat %in% c(0,1))
stopifnot(sd(c(treat))>0)
stopifnot(event %in% c(0,1))
if(any(is.na(time))) stop('No missing value is expected in time.')
if(any(is.na(event))) stop('No missing value is expected in event.')
if(any(is.na(treat))) stop('No missing value is expected in treat.')
if(any(is.na(bio))) stop('No missing value is expected in bio.')
if(!is.null(covar)) {
if(any(is.na(covar))) stop('No missing value is expected in covar.')
if(!is.matrix(covar)) stop('covar needs to be a numeric matrix.')
}
if(is.null(covar)) {
n.covar=0
} else {
covar=cbind(covar)
n.covar=ncol(covar)
stopifnot(n.covar>0)
stopifnot(nrow(covar)==length(time))
}
bio=cbind(bio)
n.bio=ncol(bio)
# numbers
n.vars=1+n.covar+n.bio+n.bio
n=length(treat)
# survival data
surv = Surv(time,event)
# construct x
x=cbind(treat,covar,bio)
index.treat=1 # treat
index.bio =1+n.covar+(1:n.bio) # biomarkers
pf = rep(0,n.vars)
pf[index.bio]=1
# glmnet regression
lam = cv.glmnet(x=x,y=surv,family="cox",alpha=alpha,standardize=FALSE,
penalty.factor=pf,nlambda=200,nfolds=nfolds)$lambda.min
fit = glmnet(x=x,y=surv,family="cox",alpha=alpha,standardize=F,penalty.factor=pf,nlambda=200)
lam.best= fit$lambda[which.min(abs(fit$lambda-lam))]
coefs = coef(fit,s=lam.best)[,1]
index.selected = abs(coefs)>0
coefs.selected = coefs[index.selected]
xx = x[,index.selected]
fit.selected = coxph(surv~xx,init=coefs.selected,iter=0)
sfit = survfit(fit.selected,newdata=as.data.frame(xx))
return(list(fit=fit,lam.best=lam.best,fit.selected=fit.selected,sfit=sfit))
}
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