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WARDEN: Workflows for health technology Assessments in R using Discrete EveNts

Introduction

WARDEN is a user-friendly package that facilitates the use of discrete event simulations without resource constraints for cost-effectiveness analysis. The package supports a flexible, practical approach to discrete event simulation while keeping an acceptable performance.

The current version supports:

  • Discrete event simulation models, Markov/semi-Markov models and hybrid models using parallel and non-parallel engines
  • Seamlessly integrating data.frames and other objects into the model
  • Delayed execution of the main inputs to facilitate readability of the model
  • Implementation of structural and parameter uncertainty
  • Helper functions to facilitate drawing of time to events and the use of hazard ratios, as well as other functions to facilitate transparency
  • Performing cost-effectiveness and uncertainty analysis

It is recommended that the user checks the vignettes, first the simple Sick-Sicker-Dead model and then the more complex model for early breast cancer. The markov example shows how to run a cohort Markov model while using the same modeling framework. Similarly, a simulation based Markov model could be run. Structural and parametric uncertainty are explored in the corresponding vignette. The IPD vignette shows how WARDEN can be used when individual patient data is available.

Documentation

Have a look at the package home site for more details on documentation and specific tutorials.

For more details on the code, check our Github repository.

Installation

WARDEN can the be installed directly from this repo via

# install.packages("devtools")
devtools::install_github("jsanchezalv/WARDEN", ref="main")

Citation

If you use WARDEN, please contact the authors for the most up to date appropiate citation.

WARDEN

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Version

Install

install.packages('WARDEN')

Version

0.99.1

License

GPL (>= 3)

Last Published

December 13th, 2024

Functions in WARDEN (0.99.1)

pcond_gompertz

Survival Probaility function for conditional Gompertz distribution (lower bound only)
modify_item_seq

Modify the value of existing items
extract_from_reactions

Extract all items and events and their interactions from the event reactions list
qcond_gamma

Conditional quantile function for gamma distribution
rbeta_mse

Draw from a beta distribution based on mean and se
modify_item

Modify the value of existing items
rcond_gompertz

Draw from a conditional Gompertz distribution (lower bound only)
qbeta_mse

Draw from a beta distribution based on mean and se (quantile)
qcond_gompertz

Quantile function for conditional Gompertz distribution (lower bound only)
qcond_llogis

Conditional quantile function for loglogistic distribution
pick_val_v

Select which values should be applied in the corresponding loop for several values (vector or list).
pick_psa

Helper function to create a list with random draws or whenever a series of functions needs to be called. Can be implemented within pick_val_v.
new_event

Generate new events to be added to existing vector of events
qcond_lnorm

Conditional quantile function for lognormal distribution
modify_event

Modify the time of existing events
qcond_norm

Conditional quantile function for normal distribution
rcond_gompertz_lu

Draw from a Conditional Gompertz distribution (lower and upper bound)
qcond_weibull

Conditional quantile function for weibull distribution
qcond_exp

Conditional quantile function for exponential distribution
rdirichlet

Draw from a dirichlet distribution based on number of counts in transition. Adapted from brms::rdirichlet
run_sim_parallel

Run simulations in parallel mode (at the simulation level)
summary_results_det

Deterministic results for a specific treatment
rpoisgamma

Draw time to event (tte) from a Poisson or Poisson-Gamma (PG) Mixture/Negative Binomial (NB) Process
run_sim

Run the simulation
tte.df

Example TTE IPD data
summary_results_sens

Summary of sensitivity outputs for a treatment
rdirichlet_prob

Draw from a dirichlet distribution based on mean transition probabilities and standard errors
replicate_profiles

Replicate profiles data.frame
rgamma_mse

Draw from a gamma distribution based on mean and se
summary_results_sim

Summary of PSA outputs for a treatment
ast_as_list

Transform a substituted expression to its Abstract Syntax Tree (AST) as a list
cond_dirichlet

Calculate conditional dirichlet values
add_tte

Define events and the initial event time
add_item

Defining parameters that may be used in model calculations
cond_mvn

Calculate conditional multivariate normal values
ceac_des

Calculate the cost-effectiveness acceptability curve (CEAC) for a DES model with a PSA result
extract_elements_from_list

Extracts items and events by looking into modify_item, modify_item_seq, modify_event and new_event
luck_adj

Perform luck adjustment
extract_psa_result

Extract PSA results from a treatment
create_indicators

Creates a vector of indicators (0 and 1) for sensitivity/DSA analysis
disc_instant_v

Calculate instantaneous discounted costs or qalys for vectors
disc_cycle_v

Cycle discounting for vectors
disc_cycle

Cycle discounting
add_reactevt

Define the modifications to other events, costs, utilities, or other items affected by the occurrence of the event
evpi_des

Calculate the Expected Value of Perfect Information (EVPI) for a DES model with a PSA result
draw_tte

Draw a time to event from a list of parametric survival functions
disc_instant

Calculate instantaneous discounted costs or qalys
disc_ongoing

Calculate discounted costs and qalys between events
disc_ongoing_v

Calculate discounted costs and qalys between events for vectors