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 |
| Author: |
Xiangdong Gu and Raji Balasubramanian |
| Maintainer: |
Xiangdong Gu <ustcgxd at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
icensmis results |
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