marginalizedRisk (version 2024.1-27)

marginalized.risk: Compute Maringalized Risk

Description

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

Usage

marginalized.risk(fit.risk, marker.name, data, categorical.s, weights =
                 rep(1, nrow(data)), t = NULL, ss = NULL, verbose =
                 FALSE, t.end = NULL)
                 
marginalized.risk.cont(fit.risk, marker.name, data, 
    weights = rep(1, nrow(data)), t=NULL, ss = NULL, verbose = FALSE)
marginalized.risk.cont.2(fit.risk, marker.name, data, 
  weights=rep(1, nrow(data)), t, ss, marker.name.2, s.2, verbose=FALSE) 
marginalized.risk.cat(fit.risk, marker.name, data, weights = rep(1,
                 nrow(data)), t = NULL, verbose = FALSE, t.end = NULL)

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.

Arguments

fit.risk

A regression object where the outcome is risk of disease, e.g. y~Z+marker. Need to support predict(fit.risk)

marker.name

string

marker.name.2

string

data

A data frame containing the phase 2 data

ss

A vector of marker values

s.2

s.2

weights

Inverse prob sampling weight, optional

t

If fit.risk is Cox regression, t is the time at which distribution function will be assessed

t.end

t.end

categorical.s

TRUE if the marker is categorical, FALSE otherwise

verbose

Boolean

Details

See the vignette file for more details.

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

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

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

}


if (FALSE) {

#### 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)
    marginalized.risk(fit.risk, "wt.loss", 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)

}