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

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