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

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

## Documentation for package ‘icensmis’ version 1.5.0

- DESCRIPTION file
- Package README

## Help Pages

bayesmc | Bayesian method for high-dimensional variable selection
datasim | Simulate data including multiple outcomes from error-prone diagnostic tests or self-reports
fitsurv | Fit survival function, used for Bayesian simulation
icmis | Maximum likelihood estimation for settings of error-prone diagnostic tests and self-reported outcomes
icpower | Study design in the presence of error-prone diagnostic tests and self-reported outcomes
icpower.val | Study design in the presence of error-prone diagnostic tests and self-reported outcomes when sensitivity and specificity are unkonwn and a validation set is used
icpowerpf | Study design in the presence of interval censored outcomes (assuming perfect diagnostic tests)
icpower_weibull | Study design in the presence of error-prone diagnostic tests and self-reported outcomes for Weibull model
plot_surv | Plot survival function