coxmnar: Cox Regression with Missing Not at Random Failure Indicators

coxmnar implements estimation for the Cox proportional hazards model when the failure indicator (cause of failure) is missing not at random (MNAR), following the methodology of Liu and Liu (2026) (Statistics and Computing, 36, 112).

Features

• Adjusted Imputation (AI) ($\hat{\beta}_n^{AI}$) and Adjusted Complete (AC) ($\hat{\beta}_n^{AC}$) estimators under MNAR.
• Benchmark estimators: Complete-Case (CC), Missing-at-Random (MAR), and Full-Data (full).
• Joint maximum likelihood estimation of outcome and missingness propensity models via L-BFGS-B.
• Nadaraya-Watson kernel propensity smoothing with 5 kernel choices and automatic bandwidth selection.
• Nonparametric bootstrap and asymptotic sandwich variance estimators.
• Data simulator simulate_coxmnar_data() implementing paper simulation DGPs.

Installation

You can install the development version of coxmnar from GitHub:

# install.packages("devtools")
devtools::install_github("shikhartyagi/coxmnar")

Quick Start

library(coxmnar)

# Simulate survival data with MNAR missing failure indicators
set.seed(123)
sim_data <- simulate_coxmnar_data(n = 200, beta0 = 0.2, mechanism = "mnar", seed = 123)

# Fit Adjusted-Imputation MNAR estimator
fit <- coxmnar(
  cause ~ Z,
  time = "time",
  data = sim_data,
  method = "ai",
  B = 100L,
  seed = 123
)

summary(fit)

Citation

To cite coxmnar in publications:

citation("coxmnar")

Reference

• Liu, Y., & Liu, K. (2026). Estimation in the Cox proportional hazards model with missing not at random failure indicators. Statistics and Computing, 36, 112. doi:10.1007/s11222-026-10857-1