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heemod - Health Economic Evaluation MODelling

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. 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("reproduction", "heemod") for an exact reproduction of the analyses from the book.

This package is still a beta version.

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")

Main features

  • Graphical user interface with shiny.
  • Time-varying transition probabilities.
  • Time-varying values attached to states.
  • Heterogeneity analysis.
  • Probabilistic uncertainty analysis.
  • Deterministic sensitivity analysis.
  • Multiple state membership correction methods (life-table, half-cycle...).
  • Demographic analysis to compute population-level results.
  • Heterogeneity analysis.

Learning heemod

To get started read the introduction in vignette("introduction", "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("homogeneous", "heemod").
  • Non-homogeneous Markov model in vignette("non-homogeneous", "heemod").
  • Probabilistic uncertainty analysis in vignette("probabilistic", "heemod").
  • Deterministic sensitivity analysis in vignette("sensitivity", "heemod").
  • Heterogeneity & Demographic analysis in vignette("heterogeneity", "heemod").

Devs

Kevin Zarca and Antoine Filipović-Pierucci.

Contributors

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Install

install.packages('heemod')

Monthly Downloads

1,092

Version

0.4.0

License

GPL (>= 3)

Issues

Pull Requests

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Maintainer

Antoine FilipovicPierucci

Last Published

January 1st, 1970

Functions in heemod (0.4.0)

check_matrix

Check Markov Model Transition Matrix
check_states

Check Model States for Consistency
check_model_index

Check Model Index
clean_factors

Convert Data Frame Factor Variables to Character
compute_counts

Compute Count of Individual in Each State per Cycle
compute_values

Compute State Values per Cycle
combine_models

Combine Multiple Models
create_options_from_tabular

Create Model Options From a Tabular Input
create_model_list_from_tabular

Read Models Specified by Files
define_correlation

Define a Correlation Structure for Probabilistic Uncertainty Analysis
define_distrib

Define Parameters Distribution for Probabilistic Analysis
create_demographic_table

Read a Demographic Table
create_df_from_tabular

Load Data From a Folder Into an Environment
create_model_from_tabular

Create a heemod Model From Tabular Files Info
create_matrix_from_tabular

Create a Transition Matrix From Tabular Input
create_parameters_from_tabular

Create a Parameter Definition From Tabular Input
create_states_from_tabular

Create State Definitions From Tabular Input
discount_hack

Hack to Work Around a Discounting Issue
define_state

Define a Markov Model State
define_sensitivity

Define a Sensitivity Analysis
define_matrix

Define Transition Matrix for Markov Model
discount

Discount a Quantity Over Time
define_model

Define a Markov Model
define_state_list

Define Markov Model State List
define_multinom

Define That Parameters Belong to the Same Multinomial Distribution
define_parameters

Define Markov Model Parameters
density

Probability Density Functions for Probabilistic Uncertainty Analysis
filter_blanks

Remove Blank Rows From Table
gather_model_info

Gather Information for Running a Model From Tabular Data
eval_model_newdata

Iteratively Evaluate a Markov Model With New Parameter Values
eval_models_from_tabular

Evaluate Models From a Tabular Source
eval_model

Evaluate Markov Model
eval_matrix

Evaluate Markov Model Transition Matrix
is_csv

Check File Type
eval_state_list

Evaluate Markov Model States
get_counts

Get State Membership Counts
get_code

Display the Code to Generate an Object
look_up

Look Up Values in a Data Frame
get_matrix_order

Return Markov Model Transition Matrix Order
make_names

Make Syntactically Valid Names
get_matrix

Get Markov Model Transition Matrix
read_file

Read the accepted file formats for tabular input
list_all_same

Check if All the Elements of a List Are the Same
is.wholenumber

Check Wholenumbers
run_models_tabular

Run Analyses From Files
plot.eval_sensitivity

Plot Sensitivity Analysis
parse_multi_spec

Specify Inputs for Multiple Models From a Single File
plot.probabilistic

Plot Results of Probabilistic Analysis
plot.run_models

Plot Results of a Markov Model
eval_parameters

Evaluate Markov model parameters
eval_resample

Evaluate Resampling Definition
get_model

Get values from a specific model
get_parameter_names

Return parameters names
run_sensitivity

Run Sensitivity Analysis
wtd_summary

Weighted Summary
safe-conversion

Safely Convert From Characters to Numbers
run_models

Run one or more Markov Model
run_probabilistic

Run Probabilistic Uncertainty Analysis
get_state_names

Get State Names
get_state_number

Return Number of State
modify

Modify Object
normalize_ce

Normalize Cost and Effect
probability

Convenience Functions to Compute Probabilities
plur

Returns "s" if x > 1
who-mortality

Use WHO Mortality Rate
update-model

Run Model on New Data
get_frontier

Return Efficiency Frontier
get_init

Get Initial State Values
get_state_value_names

Return Names of State Values
heemod

heemod: Health Economic Evaluation MODelling
check_names

Check Names
acceptability_curve

Acceptability Curve from Probabilistic Analysis
summary.run_models

Summarise Markov Model Results
save_outputs

Save Model Outputs
compute_icer

Compute ICER