plot.penmodel_cmp function

Plot method for penmodel_cmp

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.

## S3 method for class '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,  ...)

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.

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.

Returns

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.

Author(s)

Yun-Hee Choi

See Also

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

Examples

## Not run:

# 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))
## End(Not run)