FamEvent (version 3.2)

summary.penmodel_cmp: Summary method for class penmodel_cmp

Description

Provides a summary of a fitted competing risks penetrance model.

Usage

# S3 method for penmodel_cmp
summary(object, correlation=FALSE, ...)

Value

Returns the object of class 'summary.penmodel_cmp', including the following summary values:

estimates

List of parameter estimates of transformed baseline parameters and regression coefficients, their standard errors, their robust standard errors if robust=TRUE was selected when fitting the penetrance model, \(t\)-statistics and corresponding two-sided \(p\)-values.

varcov

Variance-covariance matrix of the parameter estimates.

varcov.robust

Robust variance-covariance matrix of the parameter estimates if robust = TRUE was selected when fitting the penetrance model.

correlation

Correlation matrix obtained from the variance-covariance matrix.

correlation.robust

Correlation matrix obtained from the robust variance-covariance matrix if robust = TRUE was selected when fitting the penetrance model.

Arguments

object

An object class of 'penmodel_cmp', a fitted model by penmodel_cmp function.

correlation

Logical; if TRUE, returns the correlation matrix of the estimated parameters. Default is FALSE.

...

Further arguments passed to or from other methods.

Author

Yun-Hee Choi

See Also

penmodel_cmp, print.penmodel_cmp, print.summary.penmodel_cmp plot.penmodel_cmp

Examples

# Simulated family completing risks data 

if (FALSE) {
set.seed(4321)
fam1 <- simfam_cmp(N.fam = 300, design = "pop+", variation = "frailty", competing=TRUE,
              base.dist = "Weibull", frailty.dist = "gamma", depend=c(0.5, 1), 
              allelefreq = 0.02, base.parms = list(c(0.01, 3), c(0.01, 3)), 
              vbeta = list(c(-1.13, 2.35),c(-1, 2)))

# Penetrance model fit for the simulated family data

fit <- penmodel_cmp(
       formula1 = Surv(time, status==1) ~ gender + mgene, 
       formula2 = Surv(time, status==2) ~ gender + mgene, 
       cluster = "famID", 
       parms = list(c(0.01, 3, -1.13, 2.35), c(0.01, 3, -1, 2)),
       data = fam1, design = "pop+", base.dist = "Weibull")

# Summary of the model parameter and penetrance estimates from model fit

summary(fit)
}