run_selection

This function fits a JAGS using the <code>jags</code> funciton and obtain posterior inferences.

models

Bayesian

selection

JAGS

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

run_selection function

<dl><dt>type</dt>
<dd>Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff),
Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR).</dd>
<dt>dist_e</dt>
<dd>distribution assumed for the effects. Current available chocies are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'),
Weibull ('weibull'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('nbinom') or Bernoulli ('bern').</dd>
<dt>dist_c</dt>
<dd>Distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm').</dd>
<dt>inits</dt>
<dd>a list with elements equal to the number of chains selected; each element of the list is itself a list of starting values for the BUGS model, 
or a function creating (possibly random) initial values. If inits is NULL, JAGS will generate initial values for parameters.</dd>
<dt>ppc</dt>
<dd>Logical. If <code>ppc</code> is <code>TRUE</code>, the estimates of the parameters that can be used to generate replications from the model are saved.</dd></dl>

Arguments

An internal function to execute a JAGS selection model and get posterior results — run_selection

<dl>

<dt>type</dt>
<dd>Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff),
Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR).</dd>


<dt>dist_e</dt>
<dd>distribution assumed for the effects. Current available chocies are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'),
Weibull ('weibull'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('nbinom') or Bernoulli ('bern').</dd>


<dt>dist_c</dt>
<dd>Distribution assumed for the costs. Current available chocies are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm').</dd>


<dt>inits</dt>
<dd>a list with elements equal to the number of chains selected; each element of the list is itself a list of starting values for the BUGS model, 
or a function creating (possibly random) initial values. If inits is NULL, JAGS will generate initial values for parameters.</dd>


<dt>ppc</dt>
<dd>Logical. If <code>ppc</code> is <code>TRUE</code>, the estimates of the parameters that can be used to generate replications from the model are saved.</dd>

</dl>

run_selection: An internal function to execute a JAGS selection model and get posterior results

Description

Usage

Arguments

Examples