frailtypack (version 3.4.0)

summary.longiPenal: Short summary of fixed covariates estimates of a joint model for longitudinal data and a terminal event

Description

This function returns coefficients estimates and their standard error with p-values of the Wald test for the longitudinal outcome and hazard ratios (HR) and their confidence intervals for the terminal event.

Usage

# S3 method for longiPenal
summary(object, level = 0.95, len = 6, d = 2,
lab=c("coef","hr"), ...)

Arguments

object

an object inheriting from longiPenal class

level

significance level of confidence interval. Default is 95%.

len

the total field width for the terminal part. Default is 6.

d

the desired number of digits after the decimal point. Default of 6 digits is used.

lab

labels of printed results for the longitudinal outcome and the terminal event respectively.

…

other unused arguments.

Value

For the longitudinal outcome it prints the estimates of coefficients of the fixed covariates with their standard error and p-values of the Wald test. For the terminal event it prints HR and its confidence intervals for each covariate. Confidence level is allowed (level argument).

See Also

longiPenal

Examples

# NOT RUN {

# }
# NOT RUN {
###--- Joint model for longitudinal data and a terminal event ---###

data(colorectal)
data(colorectalLongi)

# Survival data preparation - only terminal events 
colorectalSurv <- subset(colorectal, new.lesions == 0)

# Baseline hazard function approximated with splines
# Random effects as the link function

model.spli.RE <- longiPenal(Surv(time1, state) ~ age + treatment + who.PS 
+ prev.resection, tumor.size ~  year * treatment + age + who.PS ,
colorectalSurv,	data.Longi = colorectalLongi, random = c("1", "year"),
id = "id", link = "Random-effects", left.censoring = -3.33, 
n.knots = 7, kappa = 2)

# Weibull baseline hazard function
# Current level of the biomarker as the link function

model.weib.CL <- longiPenal(Surv(time1, state) ~ age + treatment + who.PS
+ prev.resection, tumor.size ~  year * treatment + age + who.PS , 
colorectalSurv, data.Longi = colorectalLongi, random = c("1", "year"),
id = "id", link = "Current-level", left.censoring = -3.33, hazard = "Weibull")
	
summary(model.spli.RE)
summary(model.weib.CL)
# }
# NOT RUN {

# }