simulations {kin.cohort}R Documentation

simulation of kin cohort studies

Description

Functions to simulate data for kin-cohort analysis

Usage


kc.simul(nfam, f, hr, rand = 0, mean.sibs = 2, mean.desc = 1.5, 
         a.age = 8, b.age = 80, a.cancer = 3, b.cancer = 180 )

sample.caco(object, p.cases = 1, caco.ratio = 1, verbose = TRUE)

## S3 method for class 'kin.cohort.sample':
summary(object,...)

Arguments

nfam number of families to be generated
f allele frequency
hr hazard ratio for disease carriers relative noncarriers
rand variance of random effect for cancer incidence (ratio of hr)
mean.sibs mean number of sibllings and descendants (~Poisson)
mean.desc mean number of sibllings and descendants (~Poisson)
a.age shape parameter for age (~Weibull)
b.age scale parameter for age (~Weibull)
a.cancer shape parameter for cancer incidence (~Weibull)
b.cancer scale parameter for cancer incidence (~Weibull)
object object of class kin.cohort.sample and data.frame
p.cases proportion of cases (affected) to include in sample. if more than 1, the exact number is assumed
caco.ratio ratio of controls per case to include in sample
verbose show the number of cases and controls sampled
... additional arguments

Details

kc.simul will generate a cohort of probands of size nfam. Default parameters simulate a typical cancer study. Each of them will be assigned: a carrier status with probability f^2+2f(1-f); a current age drawn from a Weibull distribution with parameters a.age and b.age; an age at diagnosis (agecancer) drawn from a Weibull distribution with parameters a.cancer and b.cancer, if noncarrier. For carries, the scale (b.cancer) is shifted to get the desired hazard ratio (hr). If rand>0, then a family specific random effect is also added, drawn from a normal distribution with mean 0 and sd rand. If agecancer< age then the disease status (cancer) will be 1, 0 otherwise.

First degree relatives are generated for each proband: two parents, a random number of sibblings (drawn from a Poisson withe mean mean.sibs), and a random number of descendants (drawn from a Poisson with mean mean.desc). Each of them is assiggned a carrier status with probability according to mendelian transmission conditional of the proband carrier status. Current age for relatives are generated conditional on the proband's age, with random draws from normal distribution. Age at diagnosis (agecancer) is assumed independent, except for the optional family random effect. Gender is assigned at random with probability 0.5 for all individuals.

Note that the simulation of residual familial correlation with a random effect (rand$>0) does not mantain the desired hazard ratio (hr).

The generic function summary will show the number and proportion of carriers and affected subjects in the sample.

sample.caco will sample (from a simulation generated by kc.simul) a subset of cases (afected probands) and controls (unaffected probands) and their relatives. Currently only random sampling of controls is implemented (no matching). Sampling fraction is controled by caco.ratio.

Currently, only one gene and one disease are simulated.

Value

object of class kin.cohort.sample and data.frame with fields

famid family id
rel relative type (0=proband, 1=parents, 2=sibblings, 3=descendants)
age current age of each subject
gender gender (0=male, 1=female)
carrier carrier status of proband (0=noncarrier, 1=carrier), common for all family members
cancer affected (0=no, 1=yes)
agecancer age at diagnosis or current age if not affected
real.carrier carrier status or relatives (0=noncarrier, 1=carrier )

Examples


set.seed(7)
## cohort 
s<-kc.simul(4000, f=0.03, hr=5)
summary(s)

## exclude probands
m.coh<- kc.marginal(s$agecancer, s$cancer, factor(s$carrier), s$rel,
                    knots=c(30,40,50,60,70,80,90), f=0.03)
m.coh

## relatives only
r.coh<- coxph(Surv(agecancer,cancer)~real.carrier, data=s)
print(exp(coef(r.coh)))

## probands only
p.coh<- coxph(Surv(agecancer,cancer)~carrier, data=s)
print(exp(coef(p.coh)))

## case-control
s.cc<- sample.caco(s)
summary(s.cc)

## exclude probands
m.caco<- kc.marginal(s.cc$agecancer, s.cc$cancer, factor(s.cc$carrier), 
                     s.cc$rel, knots=c(30,40,50,60,70,80,90), f=0.03)
m.caco

## relatives only
r.caco<- glm(cancer~real.carrier, family=binomial, data=s.cc, subset=(s.cc$rel!=0))
print(exp(coef(r.caco)[2]))

## probands only
p.caco<- glm(cancer~carrier, family=binomial, data=s.cc, subset=(s.cc$rel==0))
print(exp(coef(p.caco)[2]))


[Package kin.cohort version 0.6 Index]