kc.marginal {kin.cohort}R Documentation

Marginal Maximum Likelihood estimation of kin-cohort data

Description

This function estimates cumulative risk and hazard at given ages for carriers and noncarriers of a mutation based on the probands genotypes. It uses the Marginal Maximum Likelihood estimation method (Chatterjee and Wacholder, 2001). Piece-wise exponential distribution is assumed for the survival function.

Usage

kc.marginal(t, delta, genes, r, knots, f, pw = rep(1,length(t)), 
            set = NULL, B = 1, maxit = 1000, tol = 1e-5, subset,
            logrank=TRUE, trace=FALSE)

Arguments

t time variable. Usually age at diagnosis or at last follow-up
delta disease status (1: event, 0: no event
genes factor or numeric vector (1 gene), matrix or dataframe (2 genes) with genotypes of proband numeric. factors and data.frame with factors are prefered in order to use user-defined labels. Otherwise use codes (1:noncarrier, 2: carrier, 3: homozygous carrier)
r relationship with proband 1:parent, 2:sibling 3:offspring 0:proband. Probands will be excluded from analysis and offspring will be recoded 1 internally.
knots time points (ages) for cumulative risk and hazard estimates
f vector of mutation allele frequencies in the population
pw prior weights, if needed
set family id (only needed for bootstrap)
B number of boostrap samples (only needed for bootstrap)
maxit max number of iterations for the EM algorithm
tol convergence tolerance
subset logical condition to subset data
logrank Perform a logrank test
trace Show iterations for bootstrap

Value

object of classes "kin.cohort" and "chatterjee".

cumrisk matrix with cumulative risk estimates for noncarriers, carriers and the cumulative risk ratio. Estimates are given for the times indicated in the knot vector
hazard matrix with hazard estimates for noncarriers, carriers and the hazard ratio. Estimates are given for the times indicated in the knot vector
knots vector of knots
conv if the EM algorithm converged
niter number of iterations needed for convergence
ngeno.rel number of combinations of genotypes in the relatives
events matrix with number of events and person years per each knot
logHR mean log hazard ratio estimate (unweighted)
logrank logrank test. If 2 genes, for the main effects, the cross-classification and the stratified tests
call copy of call


if bootstrap confidence intervals are requested (B>1) then the returned object is of classes "kin.cohort.boot" and "chatterjee" with previous items packed in value estimate and each bootstrap sample packed in matrices.

Note

This function is best called by kin.cohort than directly

References

Chatterjee N and Wacholder S. A Marginal Likelihood Approach for Estimating Penetrance from Kin-Cohort Designs. Biometrics. 2001; 57: 245-52.

See Also

kin.cohort, print.kin.cohort, plot.kin.cohort

Examples

data(kin.data)
attach(kin.data)
res.mml<- kc.marginal(age, cancer, gen1, rel, knots=c(30,40,50,60,70,80), f=0.02)
res.mml

[Package kin.cohort version 0.6 Index]