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Fit a CoxPH Model Assuming Exchangeable Linkage Errors

Source: R/ele_coxph.R
coxphELE.Rd

Fit Cox proportional hazards regression adjusted for mismatched data based on the approach developed in Vo et al., 2024 assuming exchangeable linkage errors. Block-wise mismatch rates are assumed to be known.

Usage

coxphELE(
  x,
  y,
  cens,
  m.rate,
  blocks,
  audit.size = NULL,
  control = list(init.beta = NULL),
  ...
)

Arguments

x

A matrix or data.frame of covariates (design matrix).

y

A numeric vector of observed time-to-event outcomes.

cens

A numeric vector indicating censoring status (1 = censored, 0 = event). Note: This is the reverse of the standard Surv object convention where 1 usually indicates an event.

m.rate

block-wise mismatch rates (should be a vector with length equal to the number of blocks) - by default assume a single block.

blocks

block indicators.

audit.size

a vector of block sizes in the audit sample (selected by simple random sampling) if used to estimate the m.rate (optional). If a single value is provided, assume the same value for all blocks and put out a warning.

control

an optional list variable to of control arguments including "init.beta" for the initial outcome model coefficient estimates) - by default is the naive estimator.

...

the option to directly pass "control" arguments

Value

a list of results from the function called depending on the "family" specified.

coefficients

the outcome model coefficient estimates

var

the variance-covariance matrix

linear.predictors

the linear predictors

means

Column means of the covariate matrix x.

n

Number of observations.

nevent

Number of events.

References

Vo, T. H., Garès, V., Zhang, L. C., Happe, A., Oger, E., Paquelet, S., & Chauvet, G. (2024). Cox regression with linked data. Statistics in Medicine, 43(2), 296-314.

Examples

set.seed(101)
n <- 250

# 1. Simulate true data
age <- rnorm(n, 60, 8)
treatment <- rbinom(n, 1, 0.5)

# Simulate true hazard
# Older age and lack of treatment increase hazard
true_hazard <- exp(0.05 * (age - 60) - 0.5 * treatment)
true_time <- rexp(n, rate = true_hazard)
cens_time <- rexp(n, rate = 0.1)

obs_time <- pmin(true_time, cens_time)
status <- as.numeric(true_time <= cens_time) # 1 = event, 0 = censored

# 2. Induce 15% exchangeable linkage errors
mis_idx <- sample(1:n, size = floor(0.15 * n))
linked_age <- age
linked_treatment <- treatment
false_link_idx <- sample(1:n, size = length(mis_idx), replace = TRUE)
linked_age[mis_idx] <- age[false_link_idx]
linked_treatment[mis_idx] <- treatment[false_link_idx]

# 3. Prepare matrices for the internal routine
cens_ele <- 1 - status
X_mat <- cbind(age = linked_age, treatment = linked_treatment)

# 4. Fit the model directly
# cens = 1 for censored, 0 for event
fit_ele <- coxphELE(x = X_mat, y = obs_time, cens = cens_ele, m.rate = 0.15)
print(fit_ele$coefficients)
#>          age    treatment 
#>  0.017052210 -0.001040659