get_h_x {HQM} | R Documentation |
Local constant future conditional hazard rate estimator
Description
Calculates the local constant future hazard rate function, conditional on a marker value x
, across across a set of time values t
.
Usage
get_h_x(data, marker_name, event_time_name, time_name, event_name, x, b)
Arguments
data |
A data frame of time dependent data points. Missing values are allowed. |
marker_name |
The column name of the marker values in the data frame |
event_time_name |
The column name of the event times in the data frame |
time_name |
The column name of the times the marker values were observed in the data frame |
event_name |
The column name of the events in the data frame |
x |
Numeric value of the last observed marker value. |
b |
Bandwidth parameter. |
Details
The function get_h_x
implements the future local constant conditional hazard estimator
\hat{h}_x(t) = \frac{\sum_{i=1}^n \int_0^T\hat{\alpha}_i(X_i(t+s))Z_i(t+s)Z_i(s)K_{b}(x-X_i(s))\mathrm {d}s}{\sum_{i=1}^n\int_0^TZ_i(t+s)Z_i(s)K_{b}(x-X_i(s))\mathrm {d}s},
across a grid of possible time values t
, where X
is the marker, Z
is the exposure and \alpha(z)
is the marker-only hazard, see get_alpha
for more details.
Value
A vector of \hat h_x(t)
for a grid of possible time values t
.
See Also
Examples
library(survival)
b = 10
x = 3
Landmark <- 2
pbcT1 <- pbc2[which(pbc2$year< Landmark & pbc2$years> Landmark),]
b=0.9
arg1ll<-get_h_xll(pbcT1,'albumin',event_time_name='years',
time_name='year',event_name='status2',2,0.9)
arg1lc<-get_h_x(pbcT1,'albumin',event_time_name='years',
time_name='year',event_name='status2',2,0.9)
#Caclulate the local contant and local linear survival functions
br_s = seq(Landmark, 14, length=99)
sfalb2ll<- make_sf( (br_s[2]-br_s[1])/4 , arg1ll)
sfalb2lc<- make_sf( (br_s[2]-br_s[1])/4 , arg1lc)
#For comparison, also calculate the Kaplan-Meier
kma2<- survfit(Surv(years , status2) ~ 1, data = pbcT1)
#Plot the survival functions:
plot(br_s, sfalb2ll, type="l", col=1, lwd=2, ylab="Survival probability", xlab="Marker level")
lines(br_s, sfalb2lc, lty=2, lwd=2, col=2)
lines(kma2$time, kma2$surv, type="s", lty=2, lwd=2, col=3)
legend("topright", c( "Local linear HQM", "Local constant HQM",
"Kaplan-Meier"), lty=c(1, 2, 2), col=1:3, lwd=2, cex=1.7)