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September Workshop

This repository stores the code, presentations, and material used in the R-HTA in LMICs September Workshop. The following sections provide a breakdown of the primary documents and guidance on how to use them for your own personal training. The tutorial is based on the open-source DARTH group materials, and the majority of the code used for modelling in this tutorial is explained in the following manuscript:

The analysis folder includes the scripts with all the code, description and comments to reproduce the CEA, probabilistic sensitivity analysis (PSA) and generation of epidemiological measures of the manuscript:

The R scripts require loading functions that synthesize cSTMs outputs and conduct several sensitivity analyses included in the R folder:

  • Funtions.R: Functions to generate epidemiological measures from time-dependent cSTMs.
  • Functions_cSTM_time_dep_simulation.R: These functions wrap the simulation-time dependent cSTMs, compute CEA and epidemiological measures, and generate probabilistic sensitivity analysis (PSA) input datasets.
  • Functions_cSTM_time_dep_state_residence.R: These functions wrap the state-residence time dependent cSTMs, compute CEA and epidemiological measures, and generate probabilistic sensitivity analysis (PSA) input datasets.

Preliminaries

# Install release version from CRAN
install.packages("dampack")

# Or install development version from GitHub
# devtools::install_github("DARTH-git/dampack")
# Install release version from CRAN
install.packages("devtools")

# Or install development version from GitHub
# devtools::install_github("r-lib/devtools")
  • Install darthtools using devtools
# Install development version from GitHub
devtools::install_github("DARTH-git/darthtools")

To run the CEA, you require dampack: Decision-Analytic Modeling Package, an R package for analysing and visualizing the health economic outputs of decision models.

We strongly recommend reading the DARTH group's introductory tutorial on time-independent cSTMs in R:

Although more technical, try to also understand the use of multidimensional arrays to represent cSTM dynamics in R described in:

Lastly, we recommend familiarising with the useful DARTH coding framework described in:

Citation

This tutorial is based on the R code from Alarid-Escudero F et al. (2022)":

Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example (https://arxiv.org/abs/2108.13552). arXiv:2108.13552v2. 2022:1-37.

Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H (2022). R Code for A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example (Version v0.2.0). Zenodo. 10.5281/zenodo.6620902. Last accessed 7 June 2022.

Additional Information

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