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‎README.Rmd‎

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- Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. [An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example](http://arxiv.org/abs/2001.07824). arXiv:200107824v3. 2021:1-26.
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The release that accompanies the published article has been archived in zenodo: https://zenodo.org/record/5093594#.YPYyDy1h1qs
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The [`R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/tree/main/R) folder includes two different scripts corresponding to functions used to synthesize cSTMs outputs and conduct several sensitivity analyses:
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- [`Funtions.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions.R): Functions to generate epidemiological measures from time-dependent cSTMs.
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- [`Functions_cSTM_time_indep.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions_cSTM_time_indep.R): These functions wrap the time-dependent cSTM, compute CEA measures, and generate probabilistic sensitivity analysis (PSA) input datasets.
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## How to cite this R code in your article
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You can cite the R code in this repository like this "we based our analysis using the R code from Alarid-Escudero F et al. (2021)". Here is the full bibliographic reference to include in your reference list for the manuscript and the R code (don't forget to update the 'last accessed' date):
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> Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (http://arxiv.org/abs/2001.07824). arXiv:200107824v3. 2021:1-26.
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> Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H (2021). R Code for An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (Version v0.9.0). Zenodo. [10.5281/zenodo.5093594](https://www.doi.org/10.5281/zenodo.5093594). Last accessed 19 August 2021.
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If you adapted the code, you should indicate "Adapted from:" or "Based on" so it is understood that you modified the code. For more information on how to cite computer code, we refer the user to review [Writing Code (from MIT Research Guide)](https://integrity.mit.edu/handbook/writing-code), which provides examples of how and when to cite computer code.
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## Preliminaries
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- Install [RStudio](https://www.rstudio.com/products/rstudio/download/)
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- Install [`dampack`](https://cran.r-project.org/web/packages/dampack/index.html) R package from CRAN
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```{r, eval=FALSE}
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# Install release version from CRAN
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install.packages("dampack")
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# Or install development version from GitHub
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# devtools::install_github("DARTH-git/dampack")
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```
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- Install `devtools` to install [`darthtools`](https://github.com/DARTH-git/darthtools) R package from [DARTH's GitHub](https://github.com/DARTH-git)
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```{r, eval=FALSE}
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# Install release version from CRAN
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install.packages("devtools")
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# Or install development version from GitHub
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# devtools::install_github("r-lib/devtools")
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```
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- Install `darthtools` using `devtools`
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```{r, eval=FALSE}
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# Install development version from GitHub
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devtools::install_github("DARTH-git/darthtools")
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```
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We recommend familiarizing with the [DARTH](http://darthworkgroup.com) coding framework described in
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- Alarid-Escudero F, Krijkamp EM, Pechlivanoglou P, Jalal HJ, Kao SYZ, Yang A, Enns EA. [A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling](https://link.springer.com/article/10.1007/s40273-019-00837-x). [PharmacoEconomics](https://www.springer.com/journal/40273), 2190;37(11):1329–1339. https://doi.org/10.1007/s40273-019-00837-x
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To run the CEA, you require [`dampack`: Decision-Analytic Modeling Package](https://cran.r-project.org/web/packages/dampack/index.html), an R package for analyzing and visualizing the health economic outputs of decision models.
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## Use repository as a regular coding template
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1. On the [tutorial's GitHub repository](https://github.com/DARTH-git/cohort-modeling-tutorial-intro), navigate to the main page of the repository (https://github.com/DARTH-git/cohort-modeling-tutorial-intro).
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2. Above the file list, click **Clone or download** and select either
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a. **Open in desktop**, which requires the user to have a GitHub desktop installed, or
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b. **Download zip** that will ask the user to download the whole repository as a .zip file.
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3. Open the RStudio project `cohort-modeling-tutorial-intro.Rproj`.
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4. Install all the required packages (as mentioned above)
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- [`dampack`](https://cran.r-project.org/web/packages/dampack/index.html)
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- [`darthtools`](https://github.com/DARTH-git/darthtools)
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5. Run the scripts in the analysis folder.
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6. Modify or adapt these scripts as needed for your project or analysis.
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## Full list of Contributors:
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* [Fernando Alarid-Escudero](https://github.com/feralaes)

‎README.md‎

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Example](http://arxiv.org/abs/2001.07824). arXiv:200107824v3.
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2021:1-26.
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The release that accompanies the published article has been archived in
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zenodo: <https://zenodo.org/record/5093594#.YPYyDy1h1qs>
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The
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[`R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/tree/main/R)
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folder includes two different scripts corresponding to functions used to
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synthesize cSTMs outputs and conduct several sensitivity analyses: -
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[`Funtions.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions.R):
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Functions to generate epidemiological measures from time-dependent
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cSTMs. -
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[`Functions_cSTM_time_indep.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions_cSTM_time_indep.R):
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These functions wrap the time-dependent cSTM, compute CEA measures, and
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generate probabilistic sensitivity analysis (PSA) input datasets.
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synthesize cSTMs outputs and conduct several sensitivity analyses:
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- [`Funtions.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions.R):
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Functions to generate epidemiological measures from time-dependent
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cSTMs.
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- [`Functions_cSTM_time_indep.R`](https://github.com/DARTH-git/cohort-modeling-tutorial-intro/blob/main/R/Functions_cSTM_time_indep.R):
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These functions wrap the time-dependent cSTM, compute CEA measures,
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and generate probabilistic sensitivity analysis (PSA) input
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datasets.
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## How to cite this R code in your article
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You can cite the R code in this repository like this “we based our
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analysis using the R code from Alarid-Escudero F et al. (2021)”. Here is
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the full bibliographic reference to include in your reference list for
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the manuscript and the R code (don’t forget to update the ‘last
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accessed’ date):
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> Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM,
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> Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort
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> State-Transition Models in R Using a Cost-Effectiveness Analysis
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> Example (<http://arxiv.org/abs/2001.07824>). arXiv:200107824v3.
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> 2021:1-26.
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> Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM,
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> Pechlivanoglou P, Jalal H (2021). R Code for An Introductory Tutorial
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> on Cohort State-Transition Models in R Using a Cost-Effectiveness
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> Analysis Example (Version v0.9.0). Zenodo.
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> [10.5281/zenodo.5093594](https://www.doi.org/10.5281/zenodo.5093594).
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> Last accessed 19 August 2021.
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If you adapted the code, you should indicate “Adapted from:” or “Based
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on” so it is understood that you modified the code. For more information
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on how to cite computer code, we refer the user to review [Writing Code
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(from MIT Research
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Guide)](https://integrity.mit.edu/handbook/writing-code), which provides
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examples of how and when to cite computer code.
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## Preliminaries
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- Install
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[RStudio](https://www.rstudio.com/products/rstudio/download/)
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- Install
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[`dampack`](https://cran.r-project.org/web/packages/dampack/index.html)
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R package from CRAN
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``` r
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# Install release version from CRAN
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install.packages("dampack")
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# Or install development version from GitHub
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# devtools::install_github("DARTH-git/dampack")
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```
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- Install `devtools` to install
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[`darthtools`](https://github.com/DARTH-git/darthtools) R package
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from [DARTH’s GitHub](https://github.com/DARTH-git)
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``` r
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# Install release version from CRAN
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install.packages("devtools")
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# Or install development version from GitHub
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# devtools::install_github("r-lib/devtools")
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```
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- Install `darthtools` using `devtools`
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``` r
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# Install development version from GitHub
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devtools::install_github("DARTH-git/darthtools")
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```
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We recommend familiarizing with the [DARTH](http://darthworkgroup.com)
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coding framework described in
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R package for analyzing and visualizing the health economic outputs of
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decision models.
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## Use repository as a regular coding template
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1. On the [tutorial’s GitHub
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repository](https://github.com/DARTH-git/cohort-modeling-tutorial-intro),
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navigate to the main page of the repository
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(<https://github.com/DARTH-git/cohort-modeling-tutorial-intro>).
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2. Above the file list, click **Clone or download** and select either
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1. **Open in desktop**, which requires the user to have a GitHub
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desktop installed, or
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2. **Download zip** that will ask the user to download the whole
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repository as a .zip file.
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3. Open the RStudio project `cohort-modeling-tutorial-intro.Rproj`.
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4. Install all the required packages (as mentioned above)
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- [`dampack`](https://cran.r-project.org/web/packages/dampack/index.html)
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- [`darthtools`](https://github.com/DARTH-git/darthtools)
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5. Run the scripts in the analysis folder.
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6. Modify or adapt these scripts as needed for your project or
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analysis.
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## Full list of Contributors:
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- [Fernando Alarid-Escudero](https://github.com/feralaes)

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