Merge pull request #14 from pythonhealthdatascience/dev

Dev

Author: amyheather

README.md

@@ -1,7 +1,5 @@
 # STARS
 
-**⚠ Work in progress**
-
 This repository contains the **Quarto source used to build the [STARS project website](https://pythonhealthdatascience.github.io/stars/)**, which collates and explains research outputs from STARS.
 
 [![Sharing Tools and Artefacts for Reproducible & Reusable Simulations in healthcare](images/stars_reproduce_reuse_banner_blue.png)](https://pythonhealthdatascience.github.io/stars/)

css/theme.css

@@ -50,11 +50,27 @@ h4 {
   font-weight: bold;
 }
 
+/* Navbar/sidebar toggle button background and text/icon color */
+.quarto-secondary-nav .quarto-btn-toggle.btn {
+  background-color: #0a4568;
+  color: #ffffff;
+  margin-right: 1rem !important;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+}
+.quarto-banner nav.quarto-secondary-nav {
+  border-top: 1px solid #0a4568;
+  border-bottom: 4px solid #0a4568;
+}
+
 /* Breadcrumb items (i.e., page > page) */
+.quarto-secondary-nav nav.quarto-page-breadcrumbs a {
+  color: #cad6dd;
+}
 .quarto-title-banner a {
   color: #cad6dd;
 }
-
 .quarto-title-breadcrumbs .breadcrumb li:last-of-type a {
   color: #cad6dd;
 }

images/orcid.png

@@ -23,7 +23,7 @@ listing:
       - "../publications/2025/heather2025reproducibility/index.qmd"
 ---
 
-::: {.card}
+::: {.card-blue}
 
 **Take-home message:**
 
@@ -113,4 +113,9 @@ Amy Heather presented this work at the GW4 Open Research Prize awards event. The
 ## Publications
 
 ::: {#pub}
-:::
+:::
+
+<br>
+---
+
+*This page was written by Amy Heather and reflects her interpretation of this work, which may not fully represent the views of all project authors or affiliated institutions.*

pages/research/computational_reproducibility.qmd

@@ -21,4 +21,70 @@ author:
     orcid: 0000-0002-2204-8924
 ---
 
-Page yet to be written!
+## Summary
+
+We have developed the [DES RAP Book](https://pythonhealthdatascience.github.io/des_rap_book/), a practical guide that teaches users how to build reproducible discrete-event simulation models (DES) that fit into a reproducible analytical pipeline (RAP), with tips that benefit all types of models and analysis.
+
+Key features include:
+
+* **Dual Implementation:** Every concept is demonstrated with working code examples in both Python (SimPy) and R (simmer), allowing you to learn in your preferred language.
+
+* **Example repositories:** The book includes practical case studies like M/M/s queueing models and a stroke capacity planning model, implementing all the book recommendations and available as separate GitHub repositories.
+
+* **Built on Research:** Follows the [STARS reproducibility recommendations](/pages/research/computational_reproducibility.qmd) and the [NHS RAP Community of Practice Levels of RAP](https://nhsdigital.github.io/rap-community-of-practice/introduction_to_RAP/levels_of_RAP/).
+
+* **Interactive Learning:** Structured as a Quarto website with searchable content, code examples you can copy, and clear explanations suitable for beginners through advanced users.
+
+## Websites and GitHub repositories
+
+**DES RAP Book:**   
+[![](https://img.shields.io/badge/GitHub-des__rap__book-green?style=for-the-badge&labelColor=gray)](https://github.com/pythonhealthdatascience/des_rap_book)
+[![](https://img.shields.io/badge/DOI-10.5281/zenodo.17094155-blue?style=for-the-badge&labelColor=gray)](https://doi.org/10.5281/zenodo.17094155)
+[![](https://img.shields.io/badge/Website-des__rap__book-purple?style=for-the-badge&labelColor=gray)](https://pythonhealthdatascience.github.io/des_rap_book/)
+
+**Python M/M/s queueing model:**   
+[![](https://img.shields.io/badge/GitHub-pydesrap__mms-green?style=for-the-badge&labelColor=gray)](https://github.com/pythonhealthdatascience/pydesrap_mms)
+[![](https://img.shields.io/badge/DOI-10.5281/zenodo.14622466-blue?style=for-the-badge&labelColor=gray)](https://doi.org/10.5281/zenodo.14622466)
+
+**R M/M/s queueing model:**   
+[![](https://img.shields.io/badge/GitHub-rdesrap__mms-green?style=for-the-badge&labelColor=gray)](https://github.com/pythonhealthdatascience/rdesrap_mms)
+[![](https://img.shields.io/badge/DOI-10.5281/zenodo.14980863-blue?style=for-the-badge&labelColor=gray)](https://doi.org/10.5281/zenodo.14980863)
+
+**Python stroke model:**   
+[![](https://img.shields.io/badge/GitHub-pydesrap__stroke-green?style=for-the-badge&labelColor=gray)](https://github.com/pythonhealthdatascience/pydesrap_stroke)
+[![](https://img.shields.io/badge/DOI-10.5281/zenodo.15574906-blue?style=for-the-badge&labelColor=gray)](https://doi.org/10.5281/zenodo.15574906)
+
+**R stroke model:**   
+[![](https://img.shields.io/badge/GitHub-rdesrap__stroke-green?style=for-the-badge&labelColor=gray)](https://github.com/pythonhealthdatascience/rdesrap_stroke)
+[![](https://img.shields.io/badge/DOI-10.5281/zenodo.15863376-blue?style=for-the-badge&labelColor=gray)](https://doi.org/10.5281/zenodo.15863376)
+
+## Preview e-book
+
+```{=html}
+<div class="iframe-wrapper">
+  <div class="iframe-header">
+    <span>Website preview:</span>
+    <a href="https://pythonhealthdatascience.github.io/des_rap_book/"
+       target="_blank" rel="noopener">
+      View full website in new tab
+    </a>
+  </div>
+
+  <iframe
+    width="100%"
+    height="500"
+    src="https://pythonhealthdatascience.github.io/des_rap_book/"
+    title="DES RAP Book">
+  </iframe>
+</div>
+<br>
+```
+
+## Publications
+
+In development!
+
+<br>
+---
+
+*This page was written by Amy Heather and reflects her interpretation of this work, which may not fully represent the views of all project authors or affiliated institutions.*

pages/research/des_rap_book.qmd

@@ -1,14 +1,85 @@
 ---
 title: "DES sharing review"
+author:
+  - name: Thomas Monks
+    affiliation: University of Exeter Medical School
+    orcid: 0000-0003-2631-4481
+  - name: Alison Harper
+    affiliation: University of Exeter Business School
+    orcid: 0000-0001-5274-5037
 listing:
   - id: pub
     contents:
       - "../publications/2023/monks2023computer/index.qmd"
 ---
 
-Page yet to be written!
+## Summary
+
+Our focus in this study is on the practice of sharing healthcare DES computer models: to what extent do health researchers openly share their DES computer models, how do they do it, and what actions could the DES community take to improve what is shared?
+
+### Model sharing
+
+In total, 47 (8.3%) of the included 564 studies cited an openly available DES computer model that was used to generate their results. Of these 47, the majority (n = 29) of models were developed using FOSS tools
+
+The proportion of the literature that cited an openly available DES computer model increased from 4.0% in 2019 to 9.0% in 2022; the years 2020–2022 were similar in proportion. The figure below illustrates this, with models split by year and whether they focused on COVID-19.
+
+![Sharing of models by year of publication](des_sharing_review_resources/sharing_by_year.jpg){width=80% fig-align="center"}
+
+### Model audit
+
+The table shows the results or out audit, split by code and VIM-based models. VIM are Visual Interactive Modelling software (VIM; e.g., Arena, Anylogic, or Simio).
+
+| Practice | CODE (%) | VIM (%) |
+| - | - | - |
+| Model has DOI	| 4 (12.9) | 3 (18.8) |
+| ORCID | 3 (9.7) | 3 (18.8) |
+| Licensed | 15 (48.4) | 6 (37.5) |
+| Readme | 21 (67.7) | 7 (43.8) |
+| Steps to run | 13 (41.9) | 3 (18.8) |
+| Formal Dep Mgt | 7 (22.6) | n/a |
+| Informal Dep Mgt | 7 (22.6) | 8 (50.0) |
+| Evidence of testing | 3 (9.7) | n/a |
+| Model downloadable | 31 (100.0) | 11 (68.8) |
+| Model interactive online | 4 (12.9) | 6 (37.5) |
+
+## Online supplementary appendix
+
+We created a Jupyter Book to serve as an online supplementary appendix, providing greater detail on the review results. This can be viewed at <https://tommonks.github.io/des_sharing_lit_review>. The codebase behind this website is open and available at <https://github.com/TomMonks/des_sharing_lit_review>.
+
+```{=html}
+<div class="iframe-wrapper">
+  <div class="iframe-header">
+    <span>Website preview:</span>
+    <a href="https://tommonks.github.io/des_sharing_lit_review"
+       target="_blank" rel="noopener">
+      View full website in new tab
+    </a>
+  </div>
+
+  <iframe
+    width="100%"
+    height="500"
+    src="https://tommonks.github.io/des_sharing_lit_review"
+    title="Model and code sharing practices in healthcare discrete-event simulation - a systematic review">
+  </iframe>
+</div>
+<br>
+```
+
+## Zotero
+
+We created a Zotero online database with studies that shared computer simulation models: <https://www.zotero.org/groups/4877863/des_papers_with_code/library>.
+
+## Subsequent work
+
+Following on from this review, we developed the [STARS framework for model reuse](/pages/research/stars_framework_reuse.qmd), which provides practical guidance to help improve the reusability of shared healthcare DES models.
 
 ## Publication
 
 ::: {#pub}
-:::
+:::
+
+<br>
+---
+
+*This page was written by Amy Heather and reflects her interpretation of this work, which may not fully represent the views of all project authors or affiliated institutions.*

pages/research/des_sharing_review.qmd

@@ -1,7 +1,120 @@
 ---
 title: "Exeter Oncology Model: Renal Cell Carcinoma edition"
+author:
+  - name: Amy Heather
+    affiliation: University of Exeter Medical School
+    orcid: 0000-0002-6596-3479
 ---
 
-Exeter Oncology Model: Renal Cell Carcinoma edition (EOM-RCC)
+## Context
 
-Page yet to be written!
+The **Exeter Oncology Model: Renal Cell Carcinoma edition (EOM-RCC)** is a platform cost-effectiveness model encompassing each decision node in the disease area for advanced renal cell carcinoma.
+
+It was constructed as part of two National Institute for Health and Care Excellence (NICE) appraisals:
+
+* The NICE Pathways Pilot appraisal (ID6186, [GID-TA11186](https://www.nice.org.uk/guidance/indevelopment/gid-ta11186)).
+* The NICE appraisal of cabozantinib plus nivolumab ([TA964](https://www.nice.org.uk/guidance/ta964)) (formerly ID6184, whilst in development). 
+
+The model was developed by the Peninsula Technology Assessment Group (PenTAG) and collaborators. It's development is briefly described in:
+
+> Lee D, Burns D, Wilson E. NICE’s Pathways Pilot: Pursuing Good Decision Making in Difficult Circumstances. PharmacoEconomics - Open 2024. <https://doi.org/10.1007/s41669-024-00490-x>.
+
+The model is available on GitHub:
+
+> Lee D. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0003-4027-8456), Muthukumar M. , Lovell A., Farmer C., Burns D. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-5209-8041), Matthews J., Coelho H. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-4799-4300), O'Toole B., Trigg L. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-8447-2616), Snowsill T. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0001-7406-2819), Barnish M. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0003-0139-6548), Nikoglou T., Brand A., Ahmad Z., Abdelsabour A. [![ORCID](/images/orcid.png)](https://orcid.org/0009-0007-2532-4676), Robinson S. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0003-0463-875X), Heather A. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-6596-3479), Wilson E. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-8369-1577), Melendez-Torres G. [![ORCID](/images/orcid.png)](https://orcid.org/0000-0002-9823-4790) Exeter Oncology Model: RCC edition URL: <https://github.com/nice-digital/NICE-model-repo>.
+
+## Improving the reusability of EOM-RCC
+
+Our involvement in EOM-RCC as part of the STARS project has been to help improve the reusability of the model, including helping it adhere to the [STARS framework for model reuse](/pages/research/stars_framework_reuse.qmd).
+
+Below, we have linked to our forked versions of the GitHub repositories with these changes, but they have also been integrated into the NICE model repository linked above.
+
+### GitHub repository
+
+We can compare the repository [before](https://github.com/pythonhealthdatascience/stars-eom-rcc/tree/8e2bb675c86e86e9d1318094a880f17682d86fa4) and [after](https://github.com/pythonhealthdatascience/stars-eom-rcc) our changes, to see that amendments included that we:
+
+* Add dependency management (using `renv`).
+* Add model documentation and web app (as described below).
+* Add `CHANGELOG.md` and `CITATION.cff`
+* Amended `README.md` to incorporate information from the word document (which had instructions on how to run the model) - as well adding things like badges, ORCID IDs, repository overview, citation instructions, and funding.
+* Created releases and archived the repository on Zenodo.
+
+### Model documentation
+
+We developed a website with detailed model documentation which you can view at <https://pythonhealthdatascience.github.io/stars-eom-rcc/> (code available [on GitHub](https://github.com/pythonhealthdatascience/stars-eom-rcc/tree/main/docs)). It provides:
+
+* Acronyms.
+* Context on the associated NICE appraisals, articles and reports.
+* A detailed summary of the analysis.
+* A plain english summary of the analysis.
+* Installation instructions.
+* A step-by-step walkthrough of the code in `Model_Structure.R`.
+* Descriptions of the probabilistic analysis and scenario analysis.
+* Details about the license, citation instructions and the changelog.
+
+```{=html}
+<div class="iframe-wrapper">
+  <div class="iframe-header">
+    <span>Documentation website preview:</span>
+    <a href="https://pythonhealthdatascience.github.io/stars-eom-rcc/"
+       target="_blank" rel="noopener">
+      View full website in new tab
+    </a>
+  </div>
+
+  <iframe
+    width="100%"
+    height="500"
+    src="https://pythonhealthdatascience.github.io/stars-eom-rcc/"
+    title="EOM:RCC web app">
+  </iframe>
+</div>
+<br>
+```
+
+For example, a diagram from our [plain english summary](https://pythonhealthdatascience.github.io/stars-eom-rcc/pages/plain_english.html) of the analysis:
+
+![](eom_rcc_resources/basic_overview.drawio.png){width=80% fig-align="center"}
+
+Conversely, a diagram from the [detailed summary](https://pythonhealthdatascience.github.io/stars-eom-rcc/pages/model_overview.html):
+
+![](eom_rcc_resources/detailed_overview_vertical.drawio.png){width=80% fig-align="center"}
+
+### Web application
+
+The ambition for EOM:RCC is to create a shiny interface that allows users to interact and run the full economic model from a web application. We created pilot example web app has been created which allows users to run one part of the analysis: creating a table of possible treatment sequences.
+
+The app was developed using RShiny, with code available at <https://github.com/pythonhealthdatascience/stars-eom-rcc/tree/main/shinyapp>. The app is hosted using shinyapps at <https://amyheather.shinyapps.io/shinyapp/>.
+
+```{=html}
+<div class="iframe-wrapper">
+  <div class="iframe-header">
+    <span>Web app preview:</span>
+    <a href="https://amyheather.shinyapps.io/shinyapp/"
+       target="_blank" rel="noopener">
+      View full app in new tab
+    </a>
+  </div>
+
+  <iframe
+    width="100%"
+    height="500"
+    src="https://amyheather.shinyapps.io/shinyapp/"
+    title="EOM:RCC web app">
+  </iframe>
+</div>
+<br>
+```
+
+## Feedback
+
+::: {.card-blue}
+
+**Feedback from Dawn Lee (PenTAG):** "What you've done looks fantastic and should really help us demonstrate the potential for our bid. Wanted to say a massive thank you for all your hard work on this!"
+
+:::
+
+<br>
+---
+
+*This page was written by Amy Heather and reflects her interpretation of this work, which may not fully represent the views of all project authors or affiliated institutions.*