coxmnar: Cox Regression with Missing not at Random Failure Indicators
Implements estimation for the Cox (1972, 1975)
<doi:10.1111/j.2517-6161.1972.tb00899.x> <doi:10.1093/biomet/62.2.269>
proportional hazards model when the failure indicator (cause of failure) is
missing not at random (MNAR), following the two adjusted imputation-based
estimating equations of Liu and Liu (2026) <doi:10.1007/s11222-026-10857-1>.
Also provided for comparison are the full-data partial-likelihood estimator of
Andersen and Gill (1982) <doi:10.1214/aos/1176345976>, the complete-case
estimator, and the missing-at-random imputation estimator of Liu and Wang
(2010, Statistica Sinica, 20, 1125-1142). The probability models for the
failure indicator and for the missingness mechanism are estimated jointly by
maximum likelihood following Sun, Xie, and Liang (2013)
<doi:10.1007/s11425-012-4492-x>, and a Nadaraya-Watson kernel-smoothed
estimator of the missingness propensity is constructed following Qiu, Chen,
and Zhou (2015) <doi:10.1016/j.spl.2014.12.006>. Both an asymptotic
(sandwich-type) variance estimator and a nonparametric bootstrap variance
estimator are provided. When failure indicators are fully observed the
estimators reduce algebraically to the classical Cox partial-likelihood
estimator.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
survival (≥ 3.5-0), stats, Rdpack |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, covr |
| Published: |
2026-07-31 |
| DOI: |
10.32614/CRAN.package.coxmnar |
| Author: |
Shikhar Tyagi 
[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut] |
| Maintainer: |
Shikhar Tyagi <shikhar1093tyagi at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
coxmnar citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
coxmnar results |
Documentation:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.