nobars_

term

An internal function to separate the fixed and random effects components from an object of class formula

models

effects

random

Contains a suite of functions for health economic evaluations with missing outcome data.
The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags').
Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs.
The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

Andrea Gabrio

missingHE

Missing Outcome Data in Health Economic Evaluation

nobars_ function

<dl><dt>term</dt>
<dd>formula to be processed</dd></dl>

Arguments

An internal function to separate the fixed and random effects components from an object of class formula — nobars_

<dl>

<dt>term</dt>
<dd>formula to be processed</dd>

</dl>

nobars_: An internal function to separate the fixed and random effects components from an object of class formula

Description

Usage

Arguments

Examples