WARDEN1.2.5 package

Workflows for Health Technology Assessments in R using Discrete EveNts

add_item

Define parameters that may be used in model calculations (list)

add_item2

Define parameters that may be used in model calculations (uses express...

add_reactevt

Define the modifications to other events, costs, utilities, or other i...

add_tte

Define events and the initial event time

adj_val

Adjusted Value Calculation

ast_as_list

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

ceac_des

Calculate the cost-effectiveness acceptability curve (CEAC) for a DES ...

cond_dirichlet

Calculate conditional dirichlet values

cond_mvn

Calculate conditional multivariate normal values

create_indicators

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

disc_cycle_v

Cycle discounting for vectors

disc_cycle

Cycle discounting

disc_instant_v

Calculate instantaneous discounted costs or qalys for vectors

disc_instant

Calculate instantaneous discounted costs or qalys

disc_ongoing_v

Calculate discounted costs and qalys between events for vectors

disc_ongoing

Calculate discounted costs and qalys between events

draw_tte

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

evpi_des

Calculate the Expected Value of Perfect Information (EVPI) for a DES m...

extract_elements_from_list

Extracts items and events by looking into assignments, modify_item, mo...

extract_from_reactions

Extract all items and events and their interactions from the event rea...

extract_psa_result

Extract PSA results from a treatment

luck_adj

Perform luck adjustment

modify_event

Modify the time of existing events

modify_item_seq

Modify the value of existing items

modify_item

Modify the value of existing items

new_event

Generate new events to be added to existing vector of events

pcond_gompertz

Survival Probaility function for conditional Gompertz distribution (lo...

pick_psa

Helper function to create a list with random draws or whenever a serie...

pick_val_v

Select which values should be applied in the corresponding loop for se...

qbeta_mse

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

qcond_exp

Conditional quantile function for exponential distribution

qcond_gamma

Conditional quantile function for gamma distribution

qcond_gompertz

Quantile function for conditional Gompertz distribution (lower bound o...

qcond_llogis

Conditional quantile function for loglogistic distribution

qcond_lnorm

Conditional quantile function for lognormal distribution

qcond_norm

Conditional quantile function for normal distribution

qcond_weibull

Conditional quantile function for weibull distribution

qcond_weibullPH

Conditional quantile function for WeibullPH (flexsurv)

qgamma_mse

Use quantiles from a gamma distribution based on mean and se

qtimecov

Draw Time-to-Event with Time-Dependent Covariates and Luck Adjustment

random_stream

Creates an environment (similar to R6 class) of random uniform numbers...

rbeta_mse

Draw from a beta distribution based on mean and se

rcond_gompertz_lu

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

rcond_gompertz

Draw from a conditional Gompertz distribution (lower bound only)

rdirichlet_prob

Draw from a dirichlet distribution based on mean transition probabilit...

rdirichlet

Draw from a dirichlet distribution based on number of counts in transi...

replicate_profiles

Replicate profiles data.frame

rgamma_mse

Draw from a gamma distribution based on mean and se

rpoisgamma

Draw time to event (tte) from a Poisson or Poisson-Gamma (PG) Mixture/...

run_sim_parallel

Run simulations in parallel mode (at the simulation level)

run_sim

Run the simulation

sens_iterator

Create an iterator based on sens of the current iteration within a sce...

summary_results_det

Deterministic results for a specific treatment

summary_results_sens

Summary of sensitivity outputs for a treatment

summary_results_sim

Summary of PSA outputs for a treatment

Toolkit to support and perform discrete event simulations without resource constraints in the context of health technology assessments (HTA). The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with 'NICE TSD 15' <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>. More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

  • Maintainer: Javier Sanchez Alvarez
  • License: GPL (>= 3)
  • Last published: 2025-07-10