kc.marginal {kin.cohort}

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

## Not run: 
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

## End(Not run)