Markdown vs Quarto: Choosing the Right Tool for Clinical Research

1. Overview: Markdown vs Quarto

Markdown (MD)

Markdown is a lightweight markup language used to format text into headings, bold/italic, lists, tables, code blocks, etc.

Strengths:

• Simple syntax

• Ideal for README files, notes, tutorials

• Supported anywhere: GitHub, VS Code, RStudio, Stata logs (via dyndoc)

• Good for static documentation

Limitations:

• Limited automation

• No native support for executing code chunks

• Cannot render statistical results automatically

→ Markdown alone is NOT a dynamic document tool.

Quarto (QMD)

Quarto is a next-generation scientific/technical publishing system.It expands Markdown and adds:

• Executable code blocks (R, Stata, Python, Julia)

• Reproducible analysis

• Automatic tables, plots, citations

• Full report/book/dashboard generation

• Formats: HTML, PDF, Word, RevealJS slides, scientific manuscripts

Quarto = Markdown + Code + Rendering Engine

This makes it ideal for CECS workflows: clinical statistics, epidemiology reporting, reproducible analysis, meta-analysis, cohort modeling, and diagnostic accuracy work.

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2. Syntax Comparison

Feature	Markdown (.md)	Quarto (.qmd)
Basic text formatting	✔	✔
Code blocks	✔ (static)	✔ (dynamic)
Execute Stata code	✖	✔
Execute R code	✖	✔
Render statistical outputs	✖	✔
Create publication-ready tables/figures	✖	✔
Export HTML/PDF/Word/slides	Limited	✔ (full support)
Reproducible research	✖	✔

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3. How to Use Quarto in Stata

(Cited from your Stata Quick Reference for workflow structure )

Quarto supports native Stata execution using:

```{stata}
summarize
regress outcome exposure
```

3.1. Example Quarto Document Running Stata Code

Create: analysis.qmd

---
title: "Stata Clinical Analysis"
format: html
execute:
  echo: true
  warning: false
---

# Descriptive Statistics

```{stata}
use data.dta, clear
summarize
```

# Regression Model

```{stata}
regress sbp age bmi, robust
```

3.2. Render the document

In terminal:

quarto render analysis.qmd

---

4. How to Use Quarto in R

The Quarto engine is built into RStudio.

4.1. Example Quarto Document Using R

---
title: "R Epidemiological Modeling"
format: html
---

## Logistic Regression

```{r}
data <- read.csv("cohort.csv")
model <- glm(outcome ~ age + sex + bmi, family = binomial, data = data)
summary(model)
```

Render:

quarto::quarto_render("analysis.qmd")

---

5. Mixed R + Stata in One Quarto Document

Perfect for CECS workflow when R is used for graphics and Stata for modeling.

---
title: "Hybrid R–Stata Clinical Report"
format: pdf
---

# Stata Model

```{stata}
use trial.dta, clear
stset time, failure(event==1)
stcox treatment age sex
```

# R Visualization

```{r}
library(ggplot2)
ggplot(df, aes(age, sbp)) + geom_point()
```

This is a single reproducible document.

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6. When to Use Each

Use Markdown (.md) if:

• You are documenting code

• Writing notes

• Producing instructions or GitHub README

• Content is static

Use Quarto (.qmd) if:

• You need a reproducible analysis

• Your report includes R/Stata code + output

• You prepare manuscripts, theses, assignments

• You need PDF/HTML/Word slides from one source

• You prepare CECS-level clinical reports:

  • survival analysis

  • logistic regression

  • prediction modeling

  • recurrent events

  • meta-analysis summary calculators (your internal tools)

This matches clinical reporting standards in your internal documents (e.g., regression guides, survival workflows) .

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7. Summary

Markdown vs Quarto: Which Should Clinical Researchers Use?

Markdown is an excellent lightweight language for static documentation, but lacks computational capabilities. Quarto extends Markdown into a fully reproducible scientific publishing platform capable of executing Stata, R, Python, and Julia. In clinical epidemiology and clinical statistics—where reproducibility, transparency, and dynamic outputs are mandatory—Quarto is the superior choice.

Using Quarto, CECS researchers can integrate code, tables, survival models, regression outputs, and visualizations in a single document while ensuring full reproducibility. Both Stata and R can be called natively, enabling hybrid workflows in which Stata handles statistical modeling and R produces high-grade visualizations. Quarto thereby unifies your workflow, from raw data to a final clinical report, thesis chapter, or publication-ready manuscript.