Health Economic Evaluation Modelling: decision trees and cohort simulations. Provides a simple and consistent interface for Markov models specification, comparison, sensitivity and probabilistic analysis, input of survival models, etc. Models with time varying properties (non-homogeneous Markov models and semi-Markov models) are supported.

`heemod`

is an `R`

toolset for health economic evaluation modelling. It aims to provide a simple and consistent interface for Markov models specification and comparison (for decision trees or cohort simulation). Non-homogeneous Markov models (with time varying properties) are supported.

Most of the analyses presented in Decision Modelling for Health Economic Evaluation can be performed with `heemod`

. See `vignette("i-reproduction", "heemod")`

for an exact reproduction of the analyses from the book.

You can install:

- the latest released version from CRAN with:

install.packages("heemod")

- the latest development version from github with:

devtools::install_github("pierucci/heemod@devel")

- Graphical user interface with
`shiny`

. - Time-varying transition probabilities.
- Time-varying values attached to states.
- Microsimulation-like models.
- Probabilistic uncertainty analysis (PSA).
- Covariance analysis for PSA.
- Deterministic sensitivity analysis (DSA).
- Multiple state membership correction methods (life-table, half-cycle, etc.).
- Demographic analysis to compute population-level results.
- Heterogeneity analysis.
- Parallel computing support.

To get started read the introduction in `vignette("a-introduction", "heemod")`

. Time-varying probabilities and values are explained in `vignette("b-time-dependency", "heemod")`

.

Specific analysis examples (mostly inspired from Decision Modelling for Health Economic Evaluation) can be found in the following vignettes:

- Homogeneous Markov model in
`vignette("c-homogeneous", "heemod")`

. - Non-homogeneous Markov model in
`vignette("d-non-homogeneous", "heemod")`

. - Probabilistic uncertainty analysis in
`vignette("e-probabilistic", "heemod")`

. - Deterministic sensitivity analysis in
`vignette("f-sensitivity", "heemod")`

. - Heterogeneity & Demographic analysis in
`vignette("g-heterogeneity", "heemod")`

. - Running the models from tabular inputs in
`vignette("h-tabular", "heemod")`

.

Kevin Zarca and Antoine Filipović-Pierucci.

- Fixed an error resulting in incorrect covariance analysis results when relations between values and parameters were negative.

- Some plotting arguments changed (e.g.
`type`

=>`results`

).

- Added covariance analysis for PSA with
`type = "cov"`

plot. - All plotting functions can now represent multiple strategies with facets.
- CECA plotted on a log scale by default.
- Black & white plots for publications with the
`bw`

plot option. - Remove variables that are not sensitive in DSA with
`remove_ns`

.

- Important object structure change (from attributes to lists).
- More systematic use of getter functions.
- More standarized processing of model objects.
- Unit tests rely less on printed results.
- Using new nomenclature in backend functions (
`model`

=>`strategy`

). `base_strategy`

divided in 3 concepts:`central_strategy`

,`root_strategy`

,`noncomparable_strategy`

.- Non-heemod versions of
`discount()`

throw warnings.

- Base model cannot be specified anymore: it is always the least costly model.
- Renamed arguement
`transition_matrix`

=>`transition`

in`define_strategy()`

.

- Parallel computing with
`use_cluster()`

. - Compute average values from PSA.

- Acceptability curve returns probabilities at 0.
- Correctly identify efficiency frontier.
- ICER are computed on the efficiency frontier.

- Thanks to Matthew Wiener, Zdenek Kabat and Vojtech Filipec for their great contributions to this update.

- Added
`formatR`

to suggest list.

- Some functions were renamed for clarification:
`define_model()`

=>`define_strategy()`

`run_models()`

=>`run_model()`

`define_matrix()`

=>`define_transition()`

`define_sensitivity()`

=>`define_dsa()`

`define_distrib()`

=>`define_psa()`

`run_sensitivity()`

=>`run_dsa()`

`run_probabilistic()`

=>`run_psa()`

- Values and probabilities can depend on state time with
`state_cycle`

, allowing to reproduce the results of microsimulations. `define_sensitivity()`

now accepts any expression as input, and can call references to model parameteres.- Discount rates can now be specified as parameters (allows for rates to be modified in DSA & PSA).
- Any state value can be plotted.
- Additional output: csv files for many of the tabular results.

- Edges where P=0 are not plotted anymore for transition matrices.
- There used to be a potential error when you wanted to save output but there had been no psa.

`update()`

for heterogeneity analysis and to compute population-level values, with vignette.`run_models_tabular()`

to import models from tabular input, with vignette.`look_up()`

to look up values from external data.- Added option to pool female and male mortality rates in WHO data.
- Counting method now defaults to life-table.
`plot_sensitivity()`

now plots by default the widest bar on top.- Convenience functions for converting rates to probabilities.
- Models can be run without state values, to compute counts only.
- Much more informative error messages.
- Objects can be converted to the
`R`

code to generate them (same idea as`dput()`

, but easier to read). - New options
`heemod.verbose`

and`heemod.memotime`

. - More informative messages, especially in verbose mode.
- Use WHO data cached localy in case of connection problems.
- New functions:
`get_counts()`

and`get_init()`

to get state membership counts. - Smart sex code conversion for
`get_who_mr()`

.

*really*fixed problem when the argument to`discount()`

was not defined as a parameter.

`eval_model_newdata()`

, the function behind resampling and sensitivity analysis now returns list-variables.

- Thanks to Matthew Wiener, Zdenek Kabat and Vojtech Filipec for their great contributions to this update.

- Added a vignette to exactly reproduce results from Decision Modelling for Health Economic Evaluation.

- Fixed problem when argument to
`discount()`

was not defined as a parameter. - Corrected several errors in the vignettes (thanks to Michael Schenkenberg from SBU, Stockholm, Sweden).
- Updated mortality rate tests to reflect GHO database update.

- Fix mishandling of matrix index with
`C`

in`eval_matrix()`

(thanks to Matthew Wiener). - Fix problem with upcoming version of
`tidyr`

.

- A single set of parameters is now used for a set of models.

- Correct error in probabilistic analysis vignette.
- Fix incompatibility with upcoming
`dplyr`

update.

`shiny`

interface.- Added support for different counting options.
`get_who_mr()`

to extract mortality rates from WHO data.

- Added ability to plot matrix.
- Added framework for probabilistic uncertainty analysis.
- Written vignette for probabilistic and sensitivity analysis.
- Added complement alias for matrix definition.
`run_*`

functions now output a single table instead of a list of tables.- Variables corresponding to cost and effect must now be specified in
`run_models()`

. - Plotting for sensitivity and probabilistic analysis.

- added
`*_`

functions.

`run_model_newdata()`

cannot run`uneval_model`

anymore.- Removed need to use
`define_state_list()`

.

- Intial CRAN submission.