Plots the penetrance functions given the baseline parameter and regression coefficients' values and choices of baseline and frailty distributions.
penplot(base.parms, vbeta, variation="none", base.dist="Weibull",
frailty.dist=NULL, depend=1, agemin=20, print=TRUE, ...)Vector of parameter values for baseline hazard function.
base.parms=c(lambda, rho), where lambda and rho are the shape and scale parameters, respectively.
Vector of regression coefficients for gender and majorgene
vbeta=c(beta.s, beta.g). If variation="secondgene", specify regression coefficient for second gene in vbeta=c(beta.s, beta.g1, beta.g2).
Choice of baseline hazard distribution. Possible choices are: "Weibull", "loglogistic", "Gompertz", "lognormal", or "gamma". Default is "Weibull".
Choice of frailty distribution. Possible choices are "gamma" for gamma distribution or "lognormal" for log normal distributions when variation="frailty". Default is NULL.
Source of residual familial correlation. Possible choices are "frailty" for frailty shared within families, "secondgene" for second gene shared within families, or "none" for no residual familial correlation. Default is "none"
Variance of the frailty distribution. Dependence within families increases with depend value. Default value is 1.
Minimum age of disease onset. Default is 20 years of age.
Logical; if TRUE, prints the penetrance values by age 70 from the assumed model.
Other parameters to be passed through to plotting functions.
Displays plots of the penetrance functions and returns the following values:
Penetrance estimates by age 70 specific to gender and mutation-status subgroups.
Vetor of ages of onsest ranging from agemin to 80 years
Lists of penetrance estimates computed at each age of x.age; if variation = "none" or "frailty", lists include subgroups specific to gender and mutation status for major gene. If variation = "secondgene", lists include subgroups specific to gender and both mutation statuses for major gene and second gene.
The penetrance model conditional on the frailty \(Z\) and covariates \(X=(x_s, x_g)\) is assumed to have the following hazard function $$ h(t|X,Z) = h_0(t-t_0) Z \exp(\beta_s x_s+\beta_g x_g),$$ where \(h_0(t)\) is the baseline hazard function, \(t_0\) is a minimum age of disease onset, \(x_s\) and \(x_g\) 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 $$1-\left\{1+\frac{\lambda^\rho (t-t_0)^\rho \exp(\beta_s x_s+\beta_g x_g)}{\kappa}\right\}^{-\kappa} .$$
The penetrance curve for the second gene model is generated by $$1-\exp\left\{-\lambda^\rho (t-t_0)^\rho \exp (\beta_g x_g+\beta_{g1} x_{g1} + \beta_{g2} x_{g2}) \right\} $$ where \(x_{g1}\) indicates carrior (1) or non-carrior (0) of a major gene and \(x_{g2}\) indicates carrior (1) or non-carrior (0) of a second gene.
When plotting with the second gene model, the plot will generate separate curves for mutation carriers and noncarriers, and seperate curves for the second gene carriers and noncarriers.
# NOT RUN {
# Penetrance function curves based on Weibull baseline hazard function
penplot(base.parms=c(0.01,3), vbeta=c(0.5, 2), base.dist="Weibull", agemin=20)
# }
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