kin.cohort (version 0.7)

simulations: 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)
"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

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 )

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.

Examples

## Not run: 
# 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]))
# ## End(Not run)