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heemod - Health Economic Evaluation MODelling

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.

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")

Features

Main features:

  • Graphical user interface with shiny.
  • Accounting for time-dependency:
    • For both model time and state time.
    • Time-varying transition probabilities.
    • Time-varying values attached to states.
  • Probabilistic uncertainty analysis (PSA).
    • With correlated resampling.
    • Covariance analysis for PSA.
    • Expected value of perfect information (EVPI).
  • Deterministic sensitivity analysis (DSA).

Other features:

  • Multiple state membership correction methods (life-table, half-cycle, etc.).
  • Demographic analysis to compute population-level results.
  • Heterogeneity analysis.
  • Parallel computing support.
  • Features for budget impact analysis.

Learning heemod

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").

Devs

Kevin Zarca and Antoine Filipović-Pierucci.

Contributors

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Version

Install

install.packages('heemod')

Monthly Downloads

946

Version

0.8.0

License

GPL (>= 3)

Issues

Pull Requests

Stars

Forks

Maintainer

Antoine FilipovicPierucci

Last Published

February 11th, 2017

Functions in heemod (0.8.0)

clean_factors

Convert Data Frame Factor Variables to Character
combine_models

Combine Multiple Models
combine_probs

Combine Probabilities
check_strategy_index

Check Strategy Index
check_matrix

Check Markov Model Transition Matrix
create_options_from_tabular

Create Model Options From a Tabular Input
create_demographic_table

Read a Demographic Table
compute_values

Compute State Values per Cycle
create_parameters_from_tabular

Create a Parameter Definition From Tabular Input
create_model_from_tabular

Create a
define_dsa

Define a Sensitivity Analysis
create_model_list_from_tabular

Read Models Specified by Files
define_inflow

Define Inflow for a BIA
define_part_surv

Define Partitioned Survival
define_psa

Define Parameters Distribution for Probabilistic Analysis
define_correlation

Define a Correlation Structure for Probabilistic
compute_counts

Compute Count of Individual in Each State per Cycle
compute_icer

Compute ICER
create_states_from_tabular

Create State Definitions From Tabular Input
create_df_from_tabular

Load Data From a Folder Into an Environment
define_multinom

Define That Parameters Belong to the Same Multinomial
create_matrix_from_tabular

Create a Transition Matrix From Tabular Input
define_parameters

Define Markov Model Parameters
discount_hack

Hack to Work Around a Discounting Issue
define_transition

Define Transition Matrix for Markov Model
discount

Discount a Quantity Over Time
dispatch_strategy_hack

Hack to Automate Use of Strategy Name
eval_resample

Evaluate Resampling Definition
eval_state_list

Evaluate Markov Model States
export_savi

Export PSA Results for SAVI
get_code

Display the Code to Generate an Object
is_csv

Check File Type
get_counts.updated_model

Get State Membership Counts
filter_blanks

Remove Blank Rows From Table
get_matrix_order

Return Markov Model Transition Matrix Order
gather_model_info

Gather Information for Running a Model From Tabular Data
get_frontier

Return Efficiency Frontier
define_state

Define a Markov Model State
define_state_list

Define Markov Model State List
dispatch_strategy

Dispatch Values According to Strategy
distributions

Probability Density Functions for Probabilistic
get_parameter_names

Return parameters names
get_probs_from_surv

Extract Transition Probabilities from a Survival Model
expand_state

Expand Time-Dependant States into Tunnel States
eval_models_from_tabular

Evaluate Models From a Tabular Source
eval_parameters

Evaluate Markov model parameters
eval_transition

Evaluate Markov Model Transition Matrix
get_state_number

Return Number of State
get_state_names

Get State Names
list_all_same

Check if All the Elements of a List Are the Same
look_up

Look Up Values in a Data Frame
plur

Returns "s" if x > 1
probability

Convenience Functions to Compute Probabilities
update-model

Run Model on New Data
heemod

heemod: Health Economic Evaluation MODelling
modify

Modify Object
summary.run_model

Summarise Markov Model Results
insert

Insert Elements in Vector
make_names

Make Syntactically Valid Names
read_file

Read the accepted file formats for tabular input
rescale_discount_rate

Rescale Discount Rate
run_model

Run Markov Model
run_psa

Run Probabilistic Uncertainty Analysis
plot.psa

Plot Results of Probabilistic Analysis
plot.run_model

Plot Results of a Markov Model
safe-conversion

Safely Convert From Characters to Numbers
save_outputs

Save Model Outputs
define_strategy

Define a Markov Model
define_survival

Define a Survival Distribution
eval_strategy_newdata

Iteratively Evaluate a Markov Model With New Parameter
get_values.updated_model

Get Strategy Values
eval_strategy

Evaluate Strategy
get_state_value_names

Return Names of State Values
run_dsa

Run Sensitivity Analysis
scale.combined_model

Normalize Cost and Effect
get_transition

Get Markov Model Transition Matrix
run_model_tabular

Run Analyses From Files
as_expr_list

Convert Lazy Dots to Expression List
check_states

Check Model States for Consistency
acceptability_curve

Acceptability Curve from Probabilistic Analysis
plot.dsa

Plot Sensitivity Analysis
wtd_summary

Weighted Summary
who-mortality

Use WHO Mortality Rate
interp_heemod

Interpolate Lazy Dots
is.wholenumber

Check Wholenumbers
parse_multi_spec

Specify Inputs for Multiple Models From a Single File
check_names

Check Names
cluster

Run