kc.moments {kin.cohort}

Kin-cohort estimation of penetrance by the method of moments

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 method of moments described by Wacholder et al (1998)

Usage

kc.moments(t, delta, genes, r, knots, f, pw = rep(1,length(t)), 
           set = NULL, B = 1, logrank = TRUE, subset, trace=FALSE)

Arguments

t

time variable. Usually age at diagnosis or at last follow-up

delta

disease status (1: event, 0: no event

genes

genotype of proband numeric. A factor is preferred, otherwise numeric code of genotypes (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

mutation allele frequency in the population

pw

prior weights, if needed

set

family id (only needed for bootstrap)

B

number of boostrap samples (only needed for bootstrap)

logrank

if logrank test is desired

subset

logical condition to subset data

trace

Show iterations for bootstrap

Value

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

cumrisk

matrix of dimension (number of knots x 3) with cumulative risk festimates or noncarriers, carriers and the cumulative risk ratio

knots

vector of knots

km

object class survfit (package survival)

logrank

p-value of the logrank test

events

matrix with number of events and person years per each knot

call

copy of call

if bootstrap confidence intervals are requested (B>1) then the returned object is of classes "kin.cohort.boot" and "wacholder" 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

Wacholder S, Hartge P, Struewing JP, Pee D, McAdams M, Lawrence B, Tucker MA. The kin-cohort study for estimating penetrance. American Journal of Epidemiology. 1998; 148: 623-9.

See Also

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

Examples

## Not run: 
data(kin.data)
attach(kin.data)
res.km<- kc.moments(age, cancer, gen1, rel, knots=c(30,40,50,60,70,80), f=0.02)
res.km

## End(Not run)