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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.

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")

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...).

Learning heemod

To get started read the intro vignette (or vignette("introduction", package = "heemod")).

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

  • Homogeneous Markov model (or vignette("homogeneous", package = "heemod")).
  • Non-homogeneous Markov model (or vignette("non-homogeneous", package = "heemod")).
  • Probabilistic uncertainty analysis in vignette("probabilistic", package = "heemod").
  • Deterministic sensitivity analysis in vignette("sensitivity", package = "heemod").
  • See vignette vignette("reproduction", package = "heemod") for an exact reproduction of the analyses from the book.

Future developments

Upcoming vignettes:

  • Heterogeneity analysis in vignette("heterogeneity", package = "heemod").

Devs

Kevin Zarca and Antoine Filipović-Pierucci.

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Install

install.packages('heemod')

Monthly Downloads

1,092

Version

0.3.3

License

GPL (>= 3)

Issues

Pull Requests

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Maintainer

Antoine FilipovicPierucci

Last Published

July 1st, 2016

Functions in heemod (0.3.3)

compute_icer

Compute ICER
compute_values

Compute State Values per Cycle
check_names

Check Names
define_matrix

Define Transition Matrix for Markov Model
define_distrib

Define Parameters Distribution for Probabilistic Analysis
compute_counts

Compute Count of Individual in Each State per Cycle
define_correlation

Define a Correlation Structure for Probabilistic Uncertainty Analysis
define_model

Define a Markov Model
check_matrix

Check Markov Model Transition Matrix
check_states

Check Model States for Consistency
discount

Discount a Quantity Over Time
define_state_list

Define Markov Model State List
eval_model_newdata

Iteratively Evaluate a Markov Model With New Parameter Values
eval_model

Evaluate Markov Model
define_state

Define a Markov Model State
eval_matrix

Evaluate Markov Model Transition Matrix
define_multinom

Define That Parameters Belong to the Same Multinomial Distribution
eval_parameters

Evaluate Markov model parameters
define_sensitivity

Define a Sensitivity Analysis
define_parameters

Define Markov Model Parameters
get_parameter_names

Return parameters names
get_state_number

Return Number of State
get_frontier

Return Efficiency Frontier
get_matrix

Get Markov Model Transition Matrix
eval_resample

Evaluate Resampling Definition
eval_state_list

Evaluate Markov Model States
get_model

Get values from a specific model
get_matrix_order

Return Markov Model Transition Matrix Order
get_state_names

Get State Names
get_parameters

Get Markov Model Parameters
list_all_same

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

Check Wholenumbers
normal

Convenience Probability Density Functions for Probabilistic Analysis
plot.eval_model_list

Plot Results of Markov Model
normalize_ce

Normalize Cost and Effect
plot.probabilistic

Plot Results of Probabilistic Analysis
modify

Modify Object
get_who_mr_

Use WHO Mortality Rate
plot.eval_sensitivity

Plot Sensitivity Analysis
get_state_value_names

Return Names of State Values
run_sensitivity

Run Sensitivity Analysis
summary.eval_model_list

Summarise Markov Model Results
run_probabilistic

Run Probabilistic Uncertainty Analysis
run_newdata

Iteratively Run Markov Models Over New Parameter Sets (Heterogeneity or Probabilistic analysis)
plur

Returns "s" if x > 1
run_models

Run one or more Markov Model