summary.trivPenalNL {frailtypack}

Short summary of fixed covariates estimates of a non-linear trivariate joint model for longitudinal data, recurrent events and a terminal event

Description

This function returns coefficients estimates and their standard error with p-values of the Wald test for the biomarker growth (KG) and decline (KD) and hazard ratios and their confidence intervals for the terminal event.

Usage

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

Arguments

object

an object inheriting from trivPenal 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 (separetely for the biomarker growth and decline). For the terminal event it prints HR and its confidence intervals for each covariate. Confidence level is allowed (level argument).

See Also

trivPenalNL

Examples

## Not run: 

###--- Trivariate joint model for longitudinal data, ---###
###--- recurrent events and a terminal event ---###

data(colorectal)
data(colorectalLongi)

# Weibull baseline hazard function
# Random effects as the link function, Gap timescale
# (computation takes around 30 minutes)
model.weib.RE.gap <-trivPenal(Surv(gap.time, new.lesions) ~ cluster(id)
+ age + treatment + who.PS + prev.resection + terminal(state),
formula.terminalEvent =~ age + treatment + who.PS + prev.resection, 
tumor.size ~ year * treatment + age + who.PS, data = colorectal,
data.Longi = colorectalLongi, random = c("1", "year"), id = "id", 
link = "Random-effects", left.censoring = -3.33, recurrentAG = FALSE,
hazard = "Weibull", method.GH="Pseudo-adaptive", n.nodes = 7)

summary(model.weib.RE.gap)

## End(Not run)