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.

AuthorXiangdong Gu and Raji Balasubramanian
Date of publication2016-01-03 17:44:50
MaintainerXiangdong Gu <ustcgxd@gmail.com>
LicenseGPL (>= 2)
Version1.3.1

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Files in this package

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

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