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enhancing_workflow_efficiency_of_bio_stats.qmd
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enhancing_workflow_efficiency_of_bio_stats.qmd
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---
title: "<span style='color:white; background-color: #1fb8cc4a; font-size: 66px;'>Enhancing Efficiency and Workflow of Bio Statistics Teams</span>"
subtitle: "<span style='color:white; background-color: #1fb8cc4a; font-size: 24px;'>Converting Repeated Processes to a Shiny Suite of Applications for <br/>Reproducibility, Reporting, and Scalability </span>"
author: "<span style='color:white;'>Mayank Agrawal<br/><span style='font-size: 22px;'>Senior R developer Consultant I<br/>ProCogia</span></span>"
title-slide-attributes:
data-background-image: "data/LinkedIn_Banner_01.png"
data-background-opacity: "0.75"
date: '2023-10-27'
date-format: full
format:
revealjs:
logo: data/procogia_logo_minimal.png
footer: "R Pharma 2023"
smaller: true
slide-number: true
transition: slide
transition-speed: fast
theme: [default, custom.scss]
editor: visual
self-contained: true
---
## Phase 2 Trails
> *A Phase 2 trial answers the question, "Does Drug X improve Disease Y?"*
::: incremental
- Phase 2 clinical trials assess the [safety]{style="text-decoration:underline"} and [efficacy]{style="text-decoration:underline"} of a new drug or drug combination for a specific medical condition.
- **Goal**: Determine appropriate dose and treatment plan for Phase 3 testing.
- **Phase 2a**: Involves fewer patients, generally 100-300 patients to focus on [dose-response relationships]{style="text-decoration:underline"} and [optimal dosing frequency]{style="text-decoration:underline"}.
- **Phase 2b**: Rigorously assesses drug's effectiveness in disease treatment, prevention, or diagnosis.
- Assess **therapeutic effectiveness** in a specific patient group for potential Phase 3 study.
- Also used to assess and review **safety parameters** for **potential adverse events** that might have been missed in a particular patient group.
:::
------------------------------------------------------------------------
## Challenges
Dose Simulations for Phase 2 trails are often complex and time consuming with repeated similar workflow steps for each new variation of the dosage trail.
::: columns
::: {.column width="50%"}
::: fragment
This often leads to
- Delayed Analysis and reporting
- Longer study time (years)
- Delayed Time to Market
resulting in
- Hindered workflows
- Lower productivity
- Repeated boring processes
:::
:::
::: {.column width="50%"}
::: fragment
#### Key Issues
- Manual processes
- Lengthy simulation times
- Scalability constraints
- Limited collaboration
- Reporting challenges
- Flexibility
- Reproducibility
:::
:::
:::
{.absolute bottom="5rem" right="0" height="200"}
------------------------------------------------------------------------
## Meeting the Challenges
**Empowering Biostatisticians with discipline and the right set of tools.**
::: incremental
- Collaborated with Biostats to understand their pain points and challenges.
- An operational framework rooted in **Agile development** methodology, with a focus on **empowering Biostats** at its core.
- Enhanced workflow efficiency, reproducibility and productivity among the biostats team.
- Reproducible workflows with RStudio Projects, Git, `R`, `Shiny`, `renv` and `CRAN` packages.
- Scalable applications built with Software Engineering principles.
- Improved automated interactive reporting.
- Automated testing framework for validated analysis results.
- The framework operated like a well-oiled machine, effectively engaging Biostats with discipline and empowering them with new tools in the expanding ecosystem.
:::
------------------------------------------------------------------------
## Getting Started
::: incremental
- Define scope of each process.
- Document [repetition rate]{style="text-decoration:underline"}, [importance]{style="text-decoration:underline"}, and [time investment]{style="text-decoration:underline"} for each work request.
- Identify the most time-consuming, yet simplest workflow.
- Develop a **MVP** (Minimum Viable Product)
- Showcase a demo with the smallest workflow.
- This aids leaders in visualizing the impact of approval.
- Integrate workflows incrementally from small to large.
- Continuously improve process through rapid iterations, responding to user feedback, ideas and feature requests.
:::
{.absolute bottom="3rem" right="0" height="175"}
------------------------------------------------------------------------
## Framework Principles
::: incremental
- Showcase early application design outlines using [draw.io](draw.io){target="_blank"} for UI layout prototyping.
- Invest time to establish a standard application template layout for the ecosystem with the organizational color scheme.
- Create smaller, independent [Proof of Concepts]{style="text-decoration:underline"} (POCs) for new feature requests.
- Define the flow of reactivity for the overall application.
- Prioritize [user-friendliness]{style="text-decoration:underline"}: if it's not intuitive, it won't be used.
- Prioritize [user-requests]{style="text-decoration:underline"} based on need, impact, time, effort and complexity.
- Enable [consistent reporting]{style="text-decoration:underline"} with parameterized Markdown/Quarto for dynamic MS Word reports, following organizational templates.
- Streamline workflow by adopting standard coding principles and agile project management.
:::
------------------------------------------------------------------------
## Tools Selection
::: incremental
- `Shiny` for building interactive web apps straight from R.
- Enhanced collaboration with Biostats in `R`; their preferred language for analysis and visualization of complex clinical trial data.
- Seamless integration of [Tables, Listings, and Graphs]{style="text-decoration:underline"} (TLGs).
- Enabled real-time updates with quick release cycles, crucial for adapting to evolving trial needs.
- Enhanced decision-making thru interactive tools.
- `renv` as a package management tool to ensure reproducibility across the team and environments.
- `CRAN` published and maintained packages to ensure accurate and consistent results.
:::
------------------------------------------------------------------------
## Enhancing Scalability
::: incremental
- Implement **modularization** and **functional programming** for a [**plug-and-play**]{style="text-decoration:underline"} development format across applications.
- Enable multiple studies to be added concurrently using standard **git branching strategies**, involving multiple concurrent developers.
- [**Async Programming**]{style="text-decoration:underline"}: Evaluate longer simulations in a **separate R process** preventing app performance issues.
- Take a step further and deploy simulation functions as internal APIs with **Plumber**.
- Write your **custom JavaScript and R bindings** for implementing unique feature requests.
- Approach feature requests as a blend of **web development**, **software engineering**, and **R development**.
:::
------------------------------------------------------------------------
## Ensuring Reproducibility with Automated Testing
::: columns
::: {.column width="50%"}
::: fragment
- Writing Test Cases (Inputs, Expected Outputs)
- Full Stack Testing
- [testthat](https://github.com/r-lib/testthat/){target="_blank"} for back-end testing,
- [shinytest2](https://github.com/rstudio/shinytest2/){target="_blank"} for front end testing, and
- [shinyloadtest](https://github.com/rstudio/shinyloadtest/){target="_blank"} for load testing.
- Types of Tests: **Unit**, **Functional**, **Integration**, and **End-to-End**
- Continuous Integration for Testing with Git branching strategies.
:::
:::
::: {.column width="50%"}
::: fragment
- Benefits
- Early bug detection
- Efficiency and speed
- Consistent and repeatable testing
- Increased test coverage
- Regression testing capabilities
- Greater confidence in release stability
:::
:::
:::
------------------------------------------------------------------------
## Enhancing Adoption
::: incremental
- Invest time to create an in-depth GitHub ReadMe providing comprehensive project reproducibility instructions.
- Include application workflow [**GIFs**]{style="text-decoration:underline"} in announcement emails.
- Create detailed application interaction [**user manuals**]{style="text-decoration:underline"} with screenshots and highlights for each step.
- Conduct regular (quarterly) [**training sessions**]{style="text-decoration:underline"} to provide guidance, answer questions, and assist users with new features.
- Record and share them for easier re-visit.
- Continuously engage user base for better ROI and on boarding.
- Prioritize most requested user features for each sprint.
- Have a team of [Application Champions]{style="text-decoration:underline"} available for queries/requests.
:::
{.absolute bottom="8rem" right="0" height="150"}
------------------------------------------------------------------------
## Thank you
- Slides available on [GitHub Pages](https://mayank-procogia.github.io/r-pharma-2023/){target="_blank"} at [https://bit.ly/r-pharma-2023](https://bit.ly/r-pharma-2023){target="_blank"}
- Quarto presentation code available on [GitHub](https://github.com/mayank-procogia/r-pharma-2023){target="_blank"} at [https://bit.ly/github-r-pharma-2023](https://bit.ly/github-r-pharma-2023){target="_blank"}
- Connect and/or send me a DM for a follow up question or catch up
- Linkedin: [mayank-agrawal-7jan](https://www.linkedin.com/in/mayank-agrawal-7jan/){target="_blank"}
- X (previously Twitter): [mayank7jan](https://twitter.com/mayank7jan){target="_blank"}
- Mastodon: [mayank7j](https://fosstodon.org/@mayank7j){target="_blank"}
{.absolute bottom="5rem" right="0" height="200"}
------------------------------------------------------------------------
## References - R Packages
- [shinyDashboard](https://rstudio.github.io/shinydashboard/){target="_blank"}, [bslib](https://rstudio.github.io/bslib/){target="_blank"}, [bs4dash](https://rinterface.github.io/bs4Dash/index.html){target="_blank"} for standard dashboard template.
- [rmarkdown](https://github.com/rstudio/rmarkdown){target="_blank"} and [Quarto](https://quarto.org/docs/computations/r.html){target="_blank"} for parameterized reporting.
- [renv](https://rstudio.github.io/renv/articles/renv.html){target="_blank"} for package management in a R project.
- [glue](https://glue.tidyverse.org/){target="_blank"} for interpreted string literals for dynamic reporting.
- Async programming: [callr](https://github.com/r-lib/callr/){target="_blank"}, [mirai](https://github.com/shikokuchuo/mirai){target="_blank"}, [crew](https://github.com/wlandau/crew){target="_blank"}, [coro](https://github.com/r-lib/coro){target="_blank"}, [future](https://github.com/HenrikBengtsson/future){target="_blank"} and [promises](https://github.com/rstudio/promises/){target="_blank"}.
- [plumber](https://github.com/rstudio/plumber/){target="_blank"} for API creation.
- [httr2](https://github.com/r-lib/httr2/){target="_blank"} for API calls.
- [pins](https://github.com/rstudio/pins-r/){target="_blank"} for shareable secured publishing of data, models, and R objects
- [testthat](https://github.com/r-lib/testthat/){target="_blank"}, [shinytest2](https://github.com/rstudio/shinytest2/){target="_blank"} and [shinyloadtest](https://github.com/rstudio/shinyloadtest/){target="_blank"} for testing.
- [dplyr](https://github.com/tidyverse/dplyr/){target="_blank"} for data manipulation.
- [ggplot2](https://github.com/tidyverse/ggplot2){target="_blank"}, [plotly](https://github.com/plotly/plotly.R){target="_blank"} and [echarts4r](https://github.com/JohnCoene/echarts4r){target="_blank"} for visualization.
- [profvis](http://rstudio.github.io/profvis/){target="_blank"} for code profiling and time estimation.