haplo_surv_discrete: Discrete Time-to-Event Haplotype Analysis

Description

Performs cycle-specific logistic regression to estimate haplotype effects on discrete time-to-event data, accounting for phase ambiguity. Given observed genotypes $G$ and unobserved haplotypes $H$, the method integrates (mixes out) over the possible haplotype configurations using the conditional probabilities $P(H|G)$.

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,
  ...
)

Value

An object of class "haplosurvd" containing:

coef: Estimated coefficients (baseline time effects and haplotype/covariate effects).
se: Standard errors of the coefficients.
var: Variance-covariance matrix.
se.robust: Robust standard errors (if available).
iid: Influence function (IID) decomposition.
ploglik: Log-likelihood at convergence.
gradient, hessian: Optimization results.
Xhap, X, Haplos: Data and design matrices used.
nid, nidhap: Number of IDs and ID-haplotype combinations.

Arguments

X: Design matrix data frame (must be sorted by id and time) containing the ID, time variable, binary response, and covariates.
y: Name of the response variable (binary, 0/1) in X.
time.name: Name of the time variable in X used for sorting and cycle definition.
Haplos: Data frame containing id, haplo1, haplo2 (haplotypes as factors), and p (probability $P(H|G)$).
id: Name of the ID variable in X and Haplos.
desnames: Names of the covariate columns in X to be used in the design matrix.
designfunc: Function that computes the design vector given haplotypes $h=(h_1, h_2)$ and covariates. Must return a vector or matrix compatible with the model.
beta: Starting values for the optimization (vector of length $p + k$, where $p$ is the number of covariate effects and $k$ is the number of time cycles).
no.opt: Logical; if TRUE, skips optimization and returns estimates based on the provided beta (useful for initialization or diagnostics).
method: Optimization method: "NR" (Newton-Raphson, default) or "nlm".
stderr: Logical; if FALSE, returns only the coefficient estimates.
designMatrix: Alternative to X and Haplos: provides the response and design matrix directly (not fully implemented).
response: Alternative to X: provides the response and design directly (not fully implemented).
idhap: Name of the ID-haplotype variable to specify different haplotypes for different IDs.
design.only: Logical; if TRUE, returns only the design matrices constructed for the analysis.
covnames: Names of covariates to extract from the object for regression output.
fam: Family of the model (default binomial, currently the only option).
weights: Weights following ID for the GLM component.
offsets: Offsets following ID for the GLM component.
idhapweights: Weights following ID-haplotype for the GLM component (Work in Progress).
...: Additional arguments passed to the optimizer (lava::NR or nlm).

Author

Thomas Scheike

Details

The survival function is computed by averaging over the possible haplotypes: $$ S(t|x,G) = E[ S(t|x,H) | G ] = \sum_{h \in G} P(h|G) S(t|x,h) $$

The discrete hazard function is modeled using logistic regression: $$ \text{logit}(P(T=t | T \geq t, x, h)) = \alpha_t + x(h) \beta $$ where $\alpha_t$ are time-specific intercepts (baseline hazards), $x(h)$ is the regression design constructed from covariates and haplotypes $h=(h_1, h_2)$, and $\beta$ are the regression coefficients.

The likelihood is maximized numerically. Standard errors are computed assuming that $P(H|G)$ is known (i.e., ignoring the uncertainty in haplotype estimation).

The design matrix over possible haplotypes is constructed by merging the covariate data $X$ with the haplotype probabilities $Haplos$ and applying a user-defined designfunc.

References

Scheike, T. H. (2024). Discrete time survival analysis with haplotype effects. mets package documentation.

Examples

Run this code

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

## Some haplotype frequencies for simulations 
data(haplo)
hapfreqs <- haplo$hapfreqs 

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

baseline <- hapfreqs$haplotype[9]
baseline

## Design function: indicator for presence of any 'types' haplotype
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)

ghaplos <- haplo$ghaplos
haploX  <- haplo$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)

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