summary.longiPenal {frailtypack}

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. If a mediation analysis was performed (option mediation set to TRUE in longiPenal) this function displays estimations of the related quantities.

Usage

## S3 method for class '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: 
###--- 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)

## End(Not run)