Description Usage Arguments Details Value Author(s) References See Also Examples
Fits a penetrance model for family data based on a prospective likelihood with ascertainment correction and provides parameter estimates as well as the gender and mutationspecific penetrance estimates.
1 
parms 
Vector of initial values for baseline parameters.

vbeta 
Vector of initial values for regression coefficients for gender and majorgene;

data 
Data frame generated from 
design 
Study design of the family data. Possible choices are: 
base.dist 
Choice of baseline hazard distribution to fit. Possible choices are: 
robust 
Logical; if TRUE, use robust ‘sandwich’ standard errors and variance covariance matrix, otherwise use conventional standard errors and variance covariance matrix. 
The penetrance model is fitted to family data with a specified baseline hazard distribution,
h(tx_s, x_g) = h_0(t) \exp(β_s x_s+β_g x_g)
where h_0(t) is the baseline hazards function specified by base.dist
, which depends on the shape and scale parameters, λ and ρ; x_s indicates male (1) and female (0) and x_g indicates carrier (1) or noncarrier (0) of a gene of interest (major gene).
For family data arising from population or clinicbased study designs (design="pop", "pop+"
, "cli"
, or "cli+"
), the parameters of the penetrance model are estimated from the ascertainmentcorrected prospective likelihood approach (Choi, Kopciuk and Briollais, 2008).
For family data arising from a twostage study design (design="twostage"
), model parameters are estimated based on the composite likelihood approach (Choi and Briollais, 2011)
Transformed baseline parameters (λ, ρ) were used for estimation; log tranformation was applied to both scale and shape parameters for "Weibull"
, "loglogistic"
, "Gompertz"
and "gamma"
baseline distributions. For "lognormal"
baseline distribution, the log transformation was applied only to shape parameter ρ, not to λ which represents the location parameter in lognormal distribution.
Calculations of standard errors and 95% confidence intervals for penetrance estimates by age 70 were based on the penetrances obtained from 1000 MonteCarlo simulations of the estimated penetrance model; for more details, see penci
.
Returns an object of class 'penmodel'
, including the following elements:
coefficients 
Parameter estimates of transformed baseline parameters (λ, ρ) and regression coefficients for gender and mutation status (β_s, β_g). 
varcov 
Variance covariance matrix of parameter estimates. If 
se 
Standard errors of parameter estimates. If 
pen70.est 
Penetrance estimates by age 70 specific to gender and mutationstatus subgroups. 
pen70.se 
Standard errors of penetrance estimates by age 70 specific to gender and mutationstatus subgroups. 
pen70.ci 
95% confidence interval for penetrance estimates by age 70 specific to gender and mutationstatus subgroups. 
ageonset 
Vector of ages of onset ranging from 
pen.maleCarr 
Vector of penetrance estimates for male carriers from 
pen.femaleCarr 
Vector of penetrance estimates for female carriers from 
pen.maleNonCarr 
Vector of penetrance estimates for male noncarriers from 
pen.femaleNonCarr 
Vector of penetrance estimates for female noncarriers from 
logLik 
Loglikelihood value for the fitted penetrance model. 
YunHee Choi
Choi, Y.H., Kopciuk, K. and Briollais, L. (2008) Estimating Disease Risk Associated Mutated Genes in FamilyBased Designs, Human Heredity 66, 238251
Choi, Y.H. and Briollais (2011) An EM Composite Likelihood Approach for Multistage Sampling of Family Data with Missing Genetic Covariates, Statistica Sinica 21, 231253
penmodelEM, simfam, penplot, print.penmodel,summary.penmodel,
print.summary.penmodel, plot.penmodel, penci, penf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # Family data simulated from populationbased design using a Weibull baseline hazard
fam < simfam(N.fam=300, design="pop+", variation="none", base.dist="Weibull",
base.parms=c(0.01,3), vbeta=c(1.13, 2.35), agemin=20, allelefreq=0.02)
# Penetrance model fit for simulated family data
fit < penmodel(parms=c(0.01, 3), vbeta=c(1.13, 2.35), data=fam,
design="pop+", base.dist="Weibull")
# Summary of the model parameter and penetrance estimates from model fit
summary(fit)
# Generate the lifetime penetrance curves from model fit for specific gender and
# mutation status groups along with their nonparametric penetrance curves
plot(fit)

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