Boot.hqm: Indexed HQM hazard estimator for one bootstrap sample

In HQM: Superefficient Estimation of Future Conditional Hazards Based on Marker Information

Indexed HQM hazard estimator for one bootstrap sample

Description

Compute the bootstrap estimate of the indexed HQM hazard estimate for a single (bootstrap) sample. The function is meant to be used internally for the calculation of confidence intervals

Usage

Boot.hqm(in.par, data, data.id, ls, X1, XX1,  event_time_name = 'years', 
                 time_name = 'year', event_name = 'status2', b, t)

Arguments

in.par	Numeric vector, the values of the indexing parameters.
data	Bootstrap data.frame.
data.id	Id-level data.frame (result of to_id).
ls	user supplied grid length on the time_name argument.
X1	List of vectors for indexing: each vector corresponds to a biomarker and contains one summary measurement per individual.
XX1	List of vectors for indexing: each vector corresponds to a single biomarker and contains its longitudinal measurements across all individuals/time points.
event_time_name	Name of event time column.
time_name	Name of observation time column.
event_name	Name of event indicator column.
b	Bandwidth parameter.
t	Conditioning level.

Value

Numeric vector with estimated hazard on the grid.

Examples

#Single instance of the bootstrap version of the bootstrap version
#of the indexed HQM estimator

b.alb = 0.9   
b.bil = 4

t.alb = 1 # refers to zero mean variables - slightly high
t.bil = 1.9 # refers to zero mean variable - high

par.alb  <- 0.0702 #0.149
par.bil <- 0.0856 #0.10

 
b = 0.42 # The result, on the indexed marker 'indmar' of 
         #\code{b_selection(pbc2, 'indmar', 'years', 'year', 'status2', I=26, seq(0.2,0.4,by=0.01))} 
t = t.alb * par.alb + t.bil *par.bil

marker_name1 <- 'albumin'
marker_name2 <-  'serBilir'
event_time_name <- 'years' 
time_name <- 'year' 
event_name <- 'status2'
id<-'id'
ls<-50

data.use<-pbc2
data.use.id<-to_id(data.use)
data.use.id<-data.use.id[complete.cases(data.use.id), ]

# mean adjust the data:
X1t=data.use[,marker_name1] -mean(data.use[, marker_name1])
XX1t=data.use.id[,marker_name1] -mean(data.use.id[, marker_name1])
X2t=data.use[,marker_name2]  -mean(data.use[, marker_name2])
XX2t=data.use.id[,marker_name2] -mean(data.use.id[, marker_name2])

X1=list(X1t, X2t)
XX1=list(XX1t, XX2t)
boot.haz<-Boot.hqm (c(par.alb,par.bil), data.use, data.use.id, ls=ls, X1, XX1,  
                    event_time_name = 'years', time_name = 'year', event_name = 'status2',   b, t)

HQM documentation built on Jan. 8, 2026, 9:08 a.m.