# Missing Outcome Data in Health Economic Evaluation

## Documentation for package ‘missingHE’ version 1.6.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

anyBars | An internal function to detect the random effects component from an object of class formula
coef.missingHE | Extract regression coefficient estimates from objects in the class 'missingHE'
data_read_hurdle | A function to read and re-arrange the data in different ways for the hurdle model
data_read_lmdm | A function to read and re-arrange the data in different ways
data_read_pattern | A function to read and re-arrange the data in different ways
data_read_selection | A function to read and re-arrange the data in different ways
diagnostic | Diagnostic checks for assessing MCMC convergence of Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'.
fb | An internal function to extract the random effects component from an object of class formula
hurdle | Full Bayesian Models to handle missingness in Economic Evaluations (Hurdle Models)
isAnyArgBar | An internal function to detect the random effects component from an object of class formula
isBar | An internal function to detect the random effects component from an object of class formula
jagsresults | An internal function to summarise results from BUGS model
lmdm | Full Bayesian Models to handle missingness in Economic Evaluations (Longitudinal Missing Data Models)
MenSS | MenSS economic data on STIs
nobars_ | An internal function to separate the fixed and random effects components from an object of class formula
pattern | Full Bayesian Models to handle missingness in Economic Evaluations (Pattern Mixture Models)
PBS | PBS economic data on intellectual disability and challenging behaviour
pic | Predictive information criteria for Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'
plot.missingHE | Plot method for the imputed data contained in the objects of class 'missingHE'
ppc | Posterior predictive checks for assessing the fit to the observed data of Bayesian models implemented in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'
print.missingHE | Print method for the posterior results contained in the objects of class 'missingHE'
prior_hurdle | An internal function to change the hyperprior parameters in the hurdle model provided by the user depending on the type of structural value mechanism and outcome distributions assumed
prior_lmdm | An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed
prior_pattern | An internal function to change the hyperprior parameters in the pattern mixture model provided by the user depending on the type of missingness mechanism and outcome distributions assumed
prior_selection | An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed
run_hurdle | An internal function to execute a JAGS hurdle model and get posterior results
run_lmdm | An internal function to execute a JAGS longitudinal missing data model and get posterior results
run_pattern | An internal function to execute a JAGS pattern mixture model and get posterior results
run_selection | An internal function to execute a JAGS selection model and get posterior results
selection | Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)
summary.missingHE | Summary method for objects in the class 'missingHE'
write_hurdle | An internal function to select which type of hurdle model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of structural value mechanism assumed and independence or joint modelling This function selects which type of model to execute.
write_lmdm | An internal function to select which type of longitudinal missing data model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.
write_pattern | An internal function to select which type of pattern mixture model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.
write_selection | An internal function to select which type of selection model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.