inst/unitTests/test_fsir.R

In jotsetung/FamAgg: Pedigree Analysis and Familial Aggregation

## Testing some stuff related to the familial standardized incidence ratio
## (FSIR) from Kerber (1995).
data(minnbreast)
##mbsub <- minnbreast
mbsub <- minnbreast[minnbreast$famid %in% c(4:19, 411, 432), ]
mbped <- mbsub[, c("famid", "id", "fatherid", "motherid", "sex")]
colnames(mbped) <- FamAgg:::.PEDCN
fad <- FAData(pedigree=mbped)
tcancer <- mbsub$cancer
names(tcancer) <- mbsub$id
trait(fad) <- tcancer

test_fsir <- function(){
    do.plot <- FALSE
    ## eventually remove that...
    affected <- fad$affected
    ped <- pedigree(fad)
    kin <- kinship(fad)
    nas <- is.na(affected)
    affected <- affected[!nas]
    ped <- ped[!nas,]
    kin <- kin[!nas, !nas]
    nas <- is.na(ped$sex)
    affected <- affected[!nas]
    ped <- ped[!nas,]
    kin <- kin[!nas, !nas]
    ## get the endage for the individuals:
    endages <- minnbreast$endage
    names(endages) <- as.character(minnbreast$id)
    endages <- endages[as.character(ped$id)]
    ## Remove all those that have an NA endage
    nas <- is.na(endages)
    endages <- endages[!nas]
    kin <- kin[!nas, !nas]
    ped <- ped[!nas, ]
    affected <- affected[!nas]
    stratMat <- FamAgg:::factor2matrix(ped$sex)
    stratMatAge <- stratMat * endages
    ## lambda would be the incidence ratio in the population.
    ## using incidence ratio from http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer/incidence-invasive#heading-Zero
    ## these are the incidence rates in UK!
    FemaleBreastC <- 155.3/100000
    MaleBreastC <- 1.1/100000
    ## and for prostate cancer in UK
    ## http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer/incidence#heading-Zero
    FemaleProstateC <- 0
    MaleProstateC <- 134.3/100000
    ## the same number for the US:
    ## http://seer.cancer.gov/statfacts/html/prost.html
    ## these are new cases per year and 100000 males.
    ## FemaleProstateCUS <- 0
    ## MaleProstateCUS <- 137.9/100000

    ## Consider:
    ## shouldn't be timeInStrata the actual time at risk for each individual? This
    ## might then be interpreted for the actual example that the strata is not just
    ## 1 and 0 for male/female, but these numbers multiplied with the endage for each
    ## participant. That way, e.g. male 1 is endage years at risk.

    lambda <- c(M=(1.1+134.4)/100000, F=155.3/100000)
    ## what would be the rates estimated from the pedigree data set?
    Counts <- table(ped$sex[ped$affected > 0])
    ## relate that to the number of M/F.
    lambdaInternal <- Counts / as.numeric(table(ped$sex))

    ## That would be the FSIR with strata being male/female
    FSIRs <- FamAgg:::doFsir(affected=affected, kin=kin,
                           lambda=lambda, timeInStrata=stratMat)
    ## the same with strata being the actual time at risk in the sex strata:
    FSIRsAge <- FamAgg:::doFsir(affected=affected, kin=kin, lambda=lambda,
                              timeInStrata=stratMatAge)
    if(do.plot){
        par(mfrow=c(1, 2))
        plot(density(FSIRs, na.rm=TRUE))
        abline(v=1, col="grey")
        plot(density(FSIRsAge, na.rm=TRUE))
        abline(v=1, col="grey")
    }
    ## we get unexpectedly large FSIRs if we don't consider the age/time at risk.
    ## Thus we have to consider also the time at risk in each stratum.
    sum(FSIRs < 20 & FSIRs > 0, na.rm=TRUE)
    ## looks better for those that are
    sum(FSIRsAge > 1, na.rm=TRUE)
    sum(FSIRsAge > 2, na.rm=TRUE)

    if(do.plot){
        ## check: boxplot for FSIRs of affected and of unaffected.
        boxplot(split(FSIRsAge, f=affected), ylab="FSIR", xlab="affected")
    }

    ## Now doing it with the method.
    ## First constract time at risk strata matrix
    stratMat <- FamAgg:::factor2matrix(fad$sex)
    stratMat <- stratMat * mbsub$endage
    allFsirs <- FamAgg:::fsir(fad, trait=trait(fad), lambda=lambda,
                              timeInStrata=stratMat)
    ## compare the FSIRs with the allFsirs:
    checkEquals(FSIRsAge, allFsirs[names(FSIRsAge)])

    ##*****************************************************************
    ##
    ## Test the simulation thing.
    set.seed(18011977)
    fsirRes <- fsirTest(fad, trait=trait(fad), lambda=lambda, timeInStrata=stratMat,
                        nsim=400)
    checkEquals(FSIRsAge, fsirRes@sim$fsir[names(FSIRsAge)])
    checkEquals(FSIRsAge, fsirRes$fsir[names(FSIRsAge)])

    ##
    head(result(fsirRes))
    length(which(result(fsirRes)$pvalue < 0.05))
    fam <- result(fsirRes)[1, "family"]
    if(do.plot){
        plotPed(fsirRes, family=fam)
        plotRes(fsirRes, id=result(fsirRes)[1, "id"])
        plotPed(fsirRes, family="411")
        plotRes(fsirRes, id="16442")
        ## interestingly, id 16442 has the largest FSIR value in that pedigree,
        ## but only a poor p-value. Most likely because not much is known for this
        ## person (i.e. only one phenotyped daughter).
    }

    ## simulation with option lowMem
    set.seed(18011977)
    fsirResLM <- fsirTest(fad, trait=trait(fad), lambda=lambda, timeInStrata=stratMat,
                          nsim=400, lowMem=TRUE)
    checkEquals(fsirRes@sim$fsir, fsirResLM@sim$fsir)
    checkEquals(fsirRes@sim$pvalue, fsirResLM@sim$pvalue)

    ## checking getters.
    ## lambda
    checkEquals(fsirRes$lambda, fsirRes@lambda)
    checkEquals(fsirRes$lambda, lambda(fsirRes))
    ## timeInStrata
    checkEquals(fsirRes@timeInStrata, timeInStrata(fsirRes))
    ## fsir
    checkEquals(fsirRes@sim$fsir, fsir(fsirRes))
    checkEquals(fsir(fsirRes), fsirRes$fsir)
    ## OK, done.

    ## get the data for id 16442
    resultForId(fsirRes, id="16442")

    ## ## That below goes eventually into the vignette!
    ## ## Check the family for some of the guys with the highest risk...
    ## id <- names(sort(allFsirs, decreasing=TRUE))[1]
    ## fam <- family(fad, id=id)
    ## if(do.plot){
    ##     plotPed(fad, family=fam[1, "family"], label1=allFsirs[as.character(fam$id)])
    ## }
    ## ## OK, so male individuals have a higher FSIR, and parents of a single affected
    ## ## child that are not in kinship with other individuals in the family.

    ## id <- names(sort(allFsirs, decreasing=TRUE))[3]
    ## fam <- family(fad, id=id)
    ## if(do.plot){
    ##     plotPed(fad, family=fam[1, "family"], label1=allFsirs[as.character(fam$id)], cex=0.5)
    ## }

    ## ## Check family 432
    ## fam <- family(fad, family="432")
    ## if(do.plot){
    ##     plotPed(fad, family=fam[1, "family"], label1=allFsirs[as.character(fam$id)], cex=0.5,
    ##             only.phenotyped=TRUE)
    ## }

    ## ## Aggregate the FSIR per family.
    ## fsirPerFamMat <- aggregate(allFsirs, by=list(fad$family), mean, na.rm=TRUE)
    ## fsirPerFam <- fsirPerFamMat[, "x"]
    ## names(fsirPerFam) <- fsirPerFamMat[, 1]

    ## head(sort(fsirPerFam, decreasing=TRUE))
    ## if(do.plot){
    ##     plot(density(fsirPerFam), main="mean FSIR per family", xlab="mean FSIR")
    ##     abline(v=1, col="grey")
    ## }

    ## ## plot for some of the top families.

    ## ## Compare FSIR to FR


    ## ## binning by time interval.
    ## endages <- mbsub$endage
    ## ## stratify in < 40 > 40
}

test_fsir_sim <- function(){
    stratMat <- FamAgg:::factor2matrix(fad$sex)
    stratMat <- stratMat * mbsub$endage
    lambda <- c(M=(1.1+134.4)/100000, F=155.3/100000)
    set.seed(18011977)
    fsirRes <- fsirTest(fad, trait=trait(fad), lambda=lambda, timeInStrata=stratMat,
                        nsim=400, lowMem=FALSE)
    set.seed(18011977)
    fsirRes2 <- fsirTest(fad, trait=trait(fad), lambda=lambda, timeInStrata=stratMat,
                        nsim=400)
    set.seed(18011977)
    fsirRes2 <- fsirTest(fad, trait=trait(fad), lambda=lambda, timeInStrata=stratMat,
                        nsim=400, lowMem=TRUE)
    checkEquals(fsirRes$fsir, fsirRes2$fsir)
}

## calculate person-time at risk:
test_slice_age <- function(){
    ## given: ages, slice that into time span.
    Ages <- c(13, 65, 45, 35, 19, 34, 49, 45, 40, 39, 17)
    AgeTable <- sliceAge(Ages)
    checkEquals(max(AgeTable[, 1]), 40)
}