pandemics (version 0.1.0)

medtime: Median of the Time Spent in Hospital by Date of Admission.

Description

From the two-dimensional estimated hazard of death/recovery, the median of the time spent in hospital is computed depending on the date of admission.

Usage

medtime(hi.zt,z1)

Value

A vector with the computed median times for each day in z1.

Arguments

hi.zt

a matrix with the estimated hazard of death+recovery (M times M), obtained evaluating the function hazard2Dmiss.

z1

(optional) a vector of indexes between 1 and M indicating the admission days to evaluate the median. If missing then z1<-c(seq(1,M-1,by=2),M-1).

Author

M.L. Gámiz, E. Mammen, M.D. Martínez-Miranda and J.P. Nielsen.

References

Gámiz, M.L., Mammen, E., Martínez-Miranda, M.D. and Nielsen, J.P. (2024b). Monitoring a developing pandemic with available data. arXiv:2308.09919.

See Also

hazard2Dmiss

Examples

# \donttest{
data('covid')
Ei.z<-covid$Hospi   # exposure for survival analysis
Oi1.z<-covid$Death  # deaths
Oi2.z<-covid$Recov  # recoveries
# compute incremental values
Oi1.z<-diff(Oi1.z)
Oi2.z<-diff(Oi2.z)
Ei.z<-Ei.z[-1]     # exposure is left as cumulative
M<-length(Ei.z)
t.grid<-z.grid<-1:M
# notification date (marker)
ddates<-covid$Date

## First compute the estimated hazard
bs<-t(c(150,150))
res.h<-hazard2Dmiss(t.grid,z.grid,Oi1.z,Oi2.z,Ei.z,bs.grid=bs,cv=FALSE)
hi.zt<-res.h$hi.zt # =hi2.zt+hi1.zt (two possible outcomes)

## Now the median time at few values of the marker (admission dates)
z1<-c(seq(1,M-1,by=30),M-1)
nz<-length(z1)
res<-medtime(hi.zt,z1)

plot(z1,res,ylab='days',xaxt = "n",type='p',pch=16,
  xlim=range(z1), xlab='Date of admission',
  main='Median time from admission to exit (death+recovery)')
axis(1,at=z1,labels=ddates[z1],cex=1.2)
# }