Learn R Programming

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 with or without resource constraints for cost-effectiveness analysis. The package supports a flexible, transparent, 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
Constrained and unconstrained models (with or without interactivity between patients)
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. It is also suggested to check the constrained resources DES example on the website.

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, or our CRAN site.

Installation

WARDEN can now be installed directly from CRAN or this repo via

install.packages("WARDEN") #CRAN version 

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

Citation

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

WARDEN

Copy Link

Version

Install

install.packages('WARDEN')

Monthly Downloads

241

Version

2.0.0

License

GPL (>= 3)

Maintainer

Javier Sanchez Alvarez

Last Published

October 13th, 2025

Functions in WARDEN (2.0.0)

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

extract_psa_result

Extract PSA results from a treatment

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

Conditional quantile function for gamma distribution

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

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.

modify_item_seq

Modify the value of existing items

Add events to the queue for a patient

Quantile function for conditional Gompertz distribution (lower bound only)

Remove the next event from the queue

print.resource_discrete

Print method for resource_discrete

Conditional quantile function for weibull distribution

Conditional quantile function for normal distribution

Calculate discounted costs and qalys between events for vectors

extract_from_reactions

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

extract_elements_from_list

Extracts items and events by looking into assignments, modify_event and new_event

Get a specific event time

Conditional quantile function for loglogistic distribution

Get the next events in the queue

Draw from a gamma distribution based on mean and se

Get the next events in the queue for a specific patient

rcond_gompertz_lu

Draw from a conditional Gompertz distribution (lower and upper bound)

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

Draw from a conditional Gompertz distribution (lower bound only)

Conditional quantile function for lognormal distribution

Run the simulation

run_sim_parallel

Run simulations in parallel mode (at the simulation level)

summary_results_sim

Summary of PSA outputs for a treatment

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

pop_and_return_event

Pop and return the next event

Modify the value of existing items

qcond_weibullPH

Conditional quantile function for WeibullPH (flexsurv)

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

Calculate instantaneous discounted costs or qalys for vectors

rdirichlet_prob

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

Calculate instantaneous discounted costs or qalys

rpoisgamma_rcpp

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

Check if the event queue is empty

Get the Size of the Event Queue

Create an iterator based on sens of the current iteration within a scenario (DSA)

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

Conditional quantile function for exponential distribution

Use quantiles from a gamma distribution based on mean and se

Draw time-to-event with time-dependent covariates and luck adjustment

Create a New Event Queue

Remove events for a patient

replicate_profiles

Replicate profiles data.frame

Creates an environment (similar to R6 class) of random uniform numbers to be drawn from

Draw from a beta distribution based on mean and se

with_write_flags_lang

Evaluate a language object with write-tracking via active bindings

Example TTE IPD data

resource_discrete

Create a discrete resource

Shared input object

summary_results_det

Deterministic results for a specific treatment

summary_results_sens

Summary of sensitivity outputs for a treatment

Calculate conditional dirichlet values

Define events and the initial event time

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

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

Adjusted Value Calculation

Define parameters that may be used in model calculations (uses expressions)

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

Calculate conditional multivariate normal values

create_indicators

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

Define or append model inputs

discrete_resource_clone

Clone independent discrete resources

.resource_discrete_from_xptr

Creates a discrete resource from an external pointer

Check if a patient has a specific event in the queue

Calculate discounted costs and qalys between events

Perform luck adjustment

Cycle discounting

Cycle discounting for vectors

Modify or add events for a patient