FamEvent (version 3.2)

plot.penmodel_cmp: Plot method for penmodel_cmp

Description

Plots penetrance curves for each event estimated from the fitted competing risks model and overlays non-parametric cumulative incidence curves estimated from the data without proabands.

Usage

# S3 method for penmodel_cmp
plot(x, agemax = 80, print = TRUE, conf.int = FALSE, 
add.CIF = TRUE, MC = 100, col = c("blue", "red", "blue", "red"), lty = c(1, 1, 2, 2), 
xlab = "Age at onset", ylab = "Penetrance", ylim = NULL,  ...)

Value

Returns the following summary values:

coefficients

Parameter estimates from the competing risks model.

pen70

Penetrance estimates by age 70, specific to gender and mutation-status subgroups.

age

Vector of ages of onsest ranging from agemin to agemax years

pen1

Penetrance estimates for event 1 at each age in age, specific to gender and mutation-status subgroups.

pen2

Penetrance estimates for event 2 at each age in age, specific to gender and mutation-status subgroups.

lower1

Lower limits of 95% confidence interval estimates for penetrance for event 1 at each age in age, specific to gender and mutation status subgroups.

upper1

Upper limits of 95% confidence interval estimates for penetrance for event 1 at each age in x.age, specific to gender and mutation status subgroups.

lower2

Lower limits of 95% confidence interval estimates for penetrance for event 2 at each age in age, specific to gender and mutation status subgroups.

upper2

Upper limits of 95% confidence interval estimates for penetrance for event 2 at each age in x.age, specific to gender and mutation status subgroups.

Arguments

x

An object class of 'penmodel', a fitted model by penmodel or penmodelEM functions.

agemax

Maximum age of disease onset or maximum age. Default is 80 years of age.

print

Logical; if TRUE, displays parameter estimates and penetrance estimates by age 70.

conf.int

Logical; if TRUE, displays 95% confidence intervals for both parametric and non-parametric penetrance estimates for each subgroup and returns their lower and upper limits.

add.CIF

Logical; if TRUE, displays cumulative incidence curves from competing event data.

MC

Number of simulated samples used to calculate confidence intervals with a Monte-Carlo method. If MC = 0, no confidence intervals will be calculated. Default value is 100.

col

Colors of lines for male carriers, female carriers, male noncarrers, and female noncarriers. Default is c("blue", "red", "blue", "red").

lty

Types of lines for male carriers, female carriers, male noncarriers, and female noncarriers. Default is c(1, 1, 2, 2).

xlab

Title for the x-axis. Default is "Age at onset".

ylab

Title for the y-axis. Default is "Penetrance".

ylim

Limits for the y-axis. Default is NULL. If NULL, ylim will be automatically determined.

...

Other parameters to be passed through to plotting functions.

Author

Yun-Hee Choi

Details

The 95% confidence intervals for the parametric penetrance curves are obtained based on simulations of the parameters, assuming a multivariate normal distribution for the estimated parameters with their variance-covariance matrix. See penetrance_cmp for more details.

See Also

penmodel_cmp, print.penmodel_cmp, summary.penmodel_cmp, print.summary.penmodel_cmp, simfam_cmp

Examples

if (FALSE) {
# Simulate family data  
set.seed(4321)
fam2 <- simfam_cmp(N.fam = 500, design = "pop+", variation = "frailty", 
                  base.dist = "Weibull", frailty.dist = "cgamma", depend=c(2, 2, 2), 
                  allelefreq = 0.02, base.parms = list(c(0.01, 3), c(0.01, 3)), 
                  vbeta = list(c(-1.13, 2.35),c(-1, 2)))

# Fit family data
fit2 <- penmodel_cmp(formula1 = Surv(time, status==1)~ gender + mgene, 
                   formula2 = Surv(time, status==2)~ gender + mgene, 
                   cluster = "famID", gvar = "mgene", frailty.dist = "cgamma",
                   parms=list(c(0.01, 3, -1, 2.3), c(0.01, 3, -1, 2), c(2, 2, 2)), 
                   data=fam2, design="pop+", base.dist="Weibull", robust=TRUE)

# Plot penetrance function curves with 95
plot(fit2, conf.int=TRUE, MC=200, ylim=c(0, 0.7))

}