haplo.surv.discrete: Discrete time to event haplo type analysis

In mets: Analysis of Multivariate Event Times

Discrete time to event haplo type analysis

Description

Can be used for logistic regression when time variable is "1" for all id.

Usage

haplo.surv.discrete(
  X = NULL,
  y = "y",
  time.name = "time",
  Haplos = NULL,
  id = "id",
  desnames = NULL,
  designfunc = NULL,
  beta = NULL,
  no.opt = FALSE,
  method = "NR",
  stderr = TRUE,
  designMatrix = NULL,
  response = NULL,
  idhap = NULL,
  design.only = FALSE,
  covnames = NULL,
  fam = binomial,
  weights = NULL,
  offsets = NULL,
  idhapweights = NULL,
  ...
)

Arguments

X	design matrix data-frame (sorted after id and time variable) with id time response and desnames
y	name of response (binary response with logistic link) from X
time.name	to sort after time for X
Haplos	(data.frame with id, haplo1, haplo2 (haplotypes (h)) and p=P(h|G)) haplotypes given as factor.
id	name of id variale from X
desnames	names for design matrix
designfunc	function that computes design given haplotypes h=(h1,h2) x(h)
beta	starting values
no.opt	optimization TRUE/FALSE
method	NR, nlm
stderr	to return only estimate
designMatrix	gives response and designMatrix directly not implemented (mush contain: p, id, idhap)
response	gives response and design directly designMatrix not implemented
idhap	name of id-hap variable to specify different haplotypes for different id
design.only	to return only design matrices for haplo-type analyses.
covnames	names of covariates to extract from object for regression
fam	family of models, now binomial default and only option
weights	weights following id for GLM
offsets	following id for GLM
idhapweights	weights following id-hap for GLM (WIP)
...	Additional arguments to lower level funtions lava::NR optimizer or nlm

Details

Cycle-specific logistic regression of haplo-type effects with known haplo-type probabilities. Given observed genotype G and unobserved haplotypes H we here mix out over the possible haplotypes using that P(H|G) is provided.

S(t|x,G)) = E( S(t|x,H) | G) = \sum_{h \in G} P(h|G) S(t|z,h)

so survival can be computed by mixing out over possible h given g.

Survival is based on logistic regression for the discrete hazard function of the form

logit(P(T=t| T \geq t, x,h)) = \alpha_t + x(h) \beta

where x(h) is a regression design of x and haplotypes h=(h_1,h_2)

Likelihood is maximized and standard errors assumes that P(H|G) is known.

The design over the possible haplotypes is constructed by merging X with Haplos and can be viewed by design.only=TRUE

Author(s)

Thomas Scheike

Examples

## some haplotypes of interest
types <- c("DCGCGCTCACG","DTCCGCTGACG","ITCAGTTGACG","ITCCGCTGAGG")

## some haplotypes frequencies for simulations 
data(hapfreqs)

www <-which(hapfreqs$haplotype %in% types)
hapfreqs$freq[www]

baseline=hapfreqs$haplotype[9]
baseline

designftypes <- function(x,sm=0) {# {{{
hap1=x[1]
hap2=x[2]
if (sm==0) y <- 1*( (hap1==types) | (hap2==types))
if (sm==1) y <- 1*(hap1==types) + 1*(hap2==types)
return(y)
}# }}}

tcoef=c(-1.93110204,-0.47531630,-0.04118204,-1.57872602,-0.22176426,-0.13836416,
0.88830288,0.60756224,0.39802821,0.32706859)

data(hHaplos)
data(haploX)

haploX$time <- haploX$times
Xdes <- model.matrix(~factor(time),haploX)
colnames(Xdes) <- paste("X",1:ncol(Xdes),sep="")
X <- dkeep(haploX,~id+y+time)
X <- cbind(X,Xdes)
Haplos <- dkeep(ghaplos,~id+"haplo*"+p)
desnames=paste("X",1:6,sep="")   # six X's related to 6 cycles 
out <- haplo.surv.discrete(X=X,y="y",time.name="time",
         Haplos=Haplos,desnames=desnames,designfunc=designftypes) 
names(out$coef) <- c(desnames,types)
out$coef
summary(out)

mets documentation built on May 29, 2024, 3:51 a.m.