marginal.risk: Compute Maringalized Risk

In marginalRisk: Estimating Marginal Risk

Description

Computes risk of disease as a function of marker s by marginalizing over a covariate vector Z.

Usage

marginal.risk(fit.risk, fit.s, data, categorical.s, weights = rep(1, nrow(data)), 
    t = NULL, ss = NULL, verbose = FALSE)

marginal.risk.cat(fit.risk, fit.s, data, weights = rep(1, nrow(data)), t = NULL,
    verbose = FALSE)

marginal.risk.cont(fit.risk, fit.s, data, weights = rep(1, nrow(data)), t = NULL, 
    ss = NULL, verbose = FALSE)

Arguments

fit.risk	A regression object where the outcome is risk of disease, e.g. y~Z+marker. Need to support predict(fit.risk)
fit.s	A regression object where the outcome is the marker of interest, e.g. marker~Z. Be sure that fit.risk and fit.s use the same data (e.g. by taking care of missing data)
data	A data frame containing the phase 2 data
ss	A vector of marker values
weights	Inverse prob sampling weight, optional
t	If fit.risk is Cox regression, t is the time at which distribution function will be assessed
categorical.s	TRUE if the marker is categorical, FALSE otherwise
verbose	Boolean

Details

See the vignette file for more details.

Value

If ss is not NULL, a vector of probabilities are returned. If ss is NULL, a matrix of two columns are returned, where the first column is the marker value and the second column is the probabilties.

Examples

#### suppose wt.loss is the marker of interest

if(requireNamespace("survival")) {

library(survival)

dat=subset(lung, !is.na(wt.loss) & !is.na(ph.ecog))

f1=Surv(time, status) ~ wt.loss + ph.ecog + age + sex
f2=wt.loss ~ ph.ecog + age + sex

fit.risk = coxph(f1, data=dat)
   fit.s = lm(f2, data=dat)
     
ss=quantile(dat$wt.loss, seq(.05,.95,by=0.01))
t0=1000
prob = marginal.risk(fit.risk, fit.s, dat, categorical.s=FALSE, t = t0, ss=ss)

plot(ss, prob, type="l", xlab="Weight loss", ylab=paste0("Probability of survival at day ", t0))

}


## Not run: 

#### Efron bootstrap to get confidence band

# store the current rng state 
save.seed <- try(get(".Random.seed", .GlobalEnv), silent=TRUE) 
if (class(save.seed)=="try-error") {set.seed(1); save.seed <- get(".Random.seed", .GlobalEnv) }      

B=10 # bootstrap replicates, 1000 is good
numCores=1 # multiple cores can speed things up
library(doParallel)
out=mclapply(1:B, mc.cores = numCores, FUN=function(seed) {   
    set.seed(seed)    
    # a simple resampling scheme here. needs to be adapted to the sampling scheme
    dat.tmp=dat[sample(row(dat), replace=TRUE),]        
    fit.risk = coxph(f1, data=dat)
    fit.s    = lm(f2, dat.tmp) 
    marginal.risk(fit.risk, fit.s, dat.tmp, categorical.s=FALSE, t = t0, ss=ss)
})
res=do.call(cbind, out)

# restore rng state 
assign(".Random.seed", save.seed, .GlobalEnv)    

# quantile bootstrap CI
ci.band=t(apply(res, 1, function(x) quantile(x, c(.025,.975))))

plot(ss, prob, type="l", xlab="Weight loss", ylab=paste0("Probability of survival at day ", t0), 
    ylim=range(ci.band))
lines(ss, ci.band[,1], lty=2)
lines(ss, ci.band[,2], lty=2)


## End(Not run)

marginalRisk documentation built on Jan. 15, 2021, 3:37 p.m.