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.

Install the latest version of this package by entering the following in R:
install.packages("icensmis")
AuthorXiangdong Gu and Raji Balasubramanian
Date of publication2016-01-03 17:44:50
MaintainerXiangdong Gu <ustcgxd@gmail.com>
LicenseGPL (>= 2)
Version1.3.1

View on CRAN

Files

tests
tests/testthat.R
tests/testthat
tests/testthat/test_power.R
src
src/loglikC.cpp
src/dataproc.cpp
src/loglikA.cpp
src/powerfuncs.cpp
src/HighDimCR.cpp
src/loglikB.cpp
src/RcppExports.cpp
NAMESPACE
R
R/HighDimCR.R R/icpower.R R/icmis.R R/datasim.R R/RcppExports.R R/icpower.val.R R/icpowerpf.R
README.md
MD5
DESCRIPTION
man
man/icmis.Rd man/datasim.Rd man/icpower.Rd man/icpowerpf.Rd man/icpower.val.Rd

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