Estimate the penetrance model and penetrance curves

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Description

This function fits the penetrance model to the family data based on a prospective likelihood with ascertainment correction and provides parameter estimates as well as the gender- and mutation-specific penetrance estimates.

Usage

1
penmodel(parms, vbeta, data, design="pop", base.dist="Weibull")

Arguments

parms

Vector of initial values for baseline parameters. parms=c(lambda, rho), where lambda and rho are the initial values for the scale and shape parameters, respectively; lambda > 0, rho > 0.

vbeta

Vector of initial values for regression coefficients for gender and majorgene. vbeta=c(beta.s, beta.g).

data

Family data structure should follow the format of the data generated from simfam. data should contain specific variables: famID, indID, generation, gender, currentage, mgene, time, status and weight; attr(data,"agemin") should be specified.

design

Study design of the family data. Possible choices are: "pop", "pop+", "cli", "cli+" or "twostage", where "pop" is for the population-based design with affected probands whose mutation status can be either carrier or non-carrier, "pop+" is similar to "pop" but with mutation carrier probands, "cli" is for the clinic-based design that includes affected probands with at least one parent and one sib affected, "cli+" is similar to "cli" but with mutation carrier probands, and "twostage" is for the two-stage design with oversampling of high risks families. Default is "pop".

base.dist

Choice of baseline hazard distribution to fit. Possible choices are: "Weibull", "loglogistic", "Gompertz", "lognormal", or "gamma". Default is "Weibull".

Details

The penetrance model is fitted to family data with a specified baseline hazard distribution,

h(t|x_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 non-carrier (0) of a gene of interest (major gene).

For family data arising from population- or clinic-based study designs (design="pop", "pop+", "cli", or "cli+"), the parameters of the penetrance model are estimated from the ascertainment-corrected prospective likelihood approach (Choi, Kopciuk and Briollais, 2008).

For family data arising from a two-stage study design (design="twostage"), model parameters are estimated based on the composite likelihood approach (Choi and Briollais, 2011)

Value

An object of class penmodel, a list including elements

parms.est

Parameter estimates of baseline parameters (λ, ρ) and regression coefficients for gender and mutation status (β_s, β_g) including their standard errors and also robust standard errors.

parms.cov

Covariance matrix of parameter estimates.

parms.se

Standard errors of parameter estimates.

parms.rcov

Robust (sandwich) covariance matrix of parameter estimates.

parms.rse

Robust standard errors of parameter estimates.

pen70.est

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

pen70.se

Standard errors of penetrance estimates by age 70 specific to gender and mutation-status subgroups.

pen70.ci

95% confidence interval for penetrance estimates by age 70 specific to gender and mutation-status groups.

ageonset

Vector of ages of onset ranging from agemin to 80 years.

pen.maleCarr

Vector of penetrance estimates for male carriers from agemin to 80 years.

pen.femaleCarr

Vector of penetrance estimates for female carriers from agemin to 80 years.

pen.maleNonCarr

Vector of penetrance estimates for male non-carriers from agemin to 80 years.

pen.femaleNonCarr

Vector of penetrance estimates for female non-carriers from agemin to 80 years.

Author(s)

Yun-Hee Choi

References

Choi, Y.-H., Kopciuk, K. and Briollais, L. (2008) Estimating Disease Risk Associated Mutated Genes in Family-Based Designs, Human Heredity 66, 238-251

Choi, Y.-H. and Briollais (2011) An EM Composite Likelihood Approach for Multistage Sampling of Family Data with Missing Genetic Covariates, Statistica Sinica 21, 231-253

See Also

simfam, penplot, summary.penmodel, plot.penmodel

Examples

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# Family data simulated from population-based 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 non-parametric penetrance curves 
     
plot(fit)