penplot_cmp {FamEvent} | R Documentation |
Plot penetrance functions from competing risk models
Description
Plots the penetrance functions from competing risk models given the values of baseline parameters and regression coefficients and choices of baseline and frailty distributions.
Usage
penplot_cmp(event, base.parms, vbeta, cuts = NULL, variation = "none",
base.dist = "Weibull", frailty.dist = NULL, depend = c(1, 1),
agemin = 20, agemax = 80, print = TRUE,
col = c("blue","red","blue","red"), lty = c(1, 1, 2, 2),
add.legend = TRUE, add.title = TRUE, x = "topleft",
y = NULL, xlab = "Age at onset", ylab = "Penetrance",
ylim = NULL, main = NULL, ...)
Arguments
event |
Event of interest for penetrance function: either 1 or 2. Default is |
base.parms |
List of vectors of parameter values for the specified baseline hazard functions for both events. For example, |
vbeta |
List of vectors of regression coefficients for gender and majorgene, |
cuts |
Vector of cut points defining the intervals when |
variation |
Source of residual familial correlation. Possible choices are: |
base.dist |
Vector of two baseline hazard distributions chosen for competing events. Possible choices are: |
frailty.dist |
Choice of frailty distribution. Possible choices are |
depend |
Vector of frailty parameter values assumed for specified frailty distribution. They corresponds inverse of variance of the frailty distribution. Dependence within families decreases with |
agemin |
Minimum age of disease onset. Default is 20 years of age. |
agemax |
Maximum age of disease onset. Default is 80 years of age. |
print |
Logical; if |
col |
Colors of lines for male carriers, female carriers, male noncarrers, and female noncarriers. Default is |
lty |
Types of lines for male carriers, female carriers, male noncarriers, and female noncarriers. Default is |
add.legend |
Logical; if |
add.title |
Logical; if |
x , y |
Position of legend; see legend. Defaults are |
xlab |
Title for the x-axis. Default is |
ylab |
Title for the y-axis. Default is |
ylim |
Limits of the y-axis. Default is |
main |
Main title of the plot. Default is |
... |
Other parameters to be passed through to plotting functions. |
Details
Cause-specific proportional hazard models
The penetrance models for competing events conditional on the covariates X = c(xs, xg) are assumed to have the following hazard functions for event :
hj(t|X) = h0j(t - t0) exp(βjs * xs + βjg * xg),
where h0j(t) is the baseline hazard function for event
,
, t0j is a minimum age of disease onset, xs and xg indicate male (1) or female (0) and carrier (1) or non-carrier (0) of a major gene of interest, respectively.
The penetrance function for the penetrance model has the form, 1 - exp(- H0(t - t0) * exp(βs * xs + βg * xg )), where H0(t) is the cumulative baseline hazard function.
Shared frailty models
The penetrance model conditional on the frailty and covariates
X = c(xs, xg) is assumed to have the following hazard function:
h(t|X,Z) = h0(t - t0) Z exp(βs * xs + βg * xg),
where h0(t) is the baseline hazard function, t0 is a minimum age of disease onset, xx and xg indicate male (1) or female (0) and carrier (1) or non-carrier (0) of a main gene of interest, respectively.
For example, when using a Weibull distribution for baseline hazard and a gamma distribution for frailty, the penetrance function has the form 1 - (1 + λρ * (t - t0)ρ * exp(βs * xs + βg * xg)/κ)-κ.
Two-gene models
The penetrance curve for the two-gene model is generated by 1 - exp(- H0(t - t0) * exp(βs * xs + β1 * x1 + β2 * x2)), where H0(t) is the cumulative baseline hazard function, x1 indicates carrior (1) or non-carrior (0) of a major gene and x2 indicates carrior (1) or non-carrior (0) of a second gene. When plotting with the two-gene model, the plot will generate separate curves for mutation carriers and noncarriers, and separate curves for the second gene carriers and noncarriers.
Value
Displays plots of the penetrance functions and returns the following values:
pen70 |
Penetrance estimates by age 70 specific to gender and mutation-status subgroups. |
x.age |
Vector of ages of onset ranging from |
pen |
Penetrance estimates computed at each age of |
Author(s)
Yun-Hee Choi
See Also
Examples
# Penetrance function curves for event 1
# based on Weibull baselines (no frailty)
penplot_cmp(event=1, base.parms = list(c(0.01,3), c(0.01, 3)),
vbeta = list(c(-1, 2), c(-1, 1)), variation = "none",
base.dist = "Weibull", agemin = 20, ylim = c(0,1))
# Penetrance function curves for event 1
# based on gamma frailty and Weibull baselines
penplot_cmp(event=1, base.parms = list(c(0.01,3), c(0.01, 3)),
vbeta = list(c(-1, 2), c(-1, 1)), depend=c(2, 2),
variation = "frailty", frailty.dist="gamma", base.dist = "Weibull",
agemin = 20, ylim = c(0,1))
# Penetrance function curves for event 1
# based on correlated gamma frailty and Weibull baselines
penplot_cmp(event=1, base.parms = list(c(0.01,3), c(0.01, 3)),
vbeta = list(c(-1, 2), c(-1, 1)), depend=c(2, 2, 0.2),
variation = "frailty", frailty.dist="cgamma",
base.dist = "Weibull", agemin = 20, ylim = c(0,1))
# Penetrance function curves for event 1
# based on correlated lognormal frailty and Weibull baselines
penplot_cmp(event=1, base.parms = list(c(0.01,3), c(0.01, 3)),
vbeta = list(c(-1, 2), c(-1, 1)), depend=c(2, 2, 0.2),
variation = "frailty", frailty.dist="clognormal",
base.dist = "Weibull", agemin = 20, ylim = c(0,1))