icensmis: Study Design and Data Analysis in the Presence of Error-Prone Diagnostic Tests and Self-Reported Outcomes

We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting. Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) <doi:10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016) <doi:10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, and Raji Balasubramanian (2020) <doi:10.1186/s12911-020-01223-w>.

Version: 1.5.0
Imports: Rcpp (≥ 0.11.3)
LinkingTo: Rcpp
Suggests: testthat
Published: 2021-09-02
DOI: 10.32614/CRAN.package.icensmis
Author: Xiangdong Gu and Raji Balasubramanian
Maintainer: Xiangdong Gu <ustcgxd at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README
CRAN checks: icensmis results


Reference manual: icensmis.pdf


Package source: icensmis_1.5.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): icensmis_1.5.0.tgz, r-oldrel (arm64): icensmis_1.5.0.tgz, r-release (x86_64): icensmis_1.5.0.tgz, r-oldrel (x86_64): icensmis_1.5.0.tgz
Old sources: icensmis archive

Reverse dependencies:

Reverse imports: icRSF


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