Vibe Coding in Clinical Research

Preface

Welcome to “Vibe Coding in Clinical Research,” a comprehensive guide to using R for biostatistical analysis in clinical settings. This book bridges the gap between theoretical biostatistics and practical implementation, with a focus on code efficiency, reproducibility, and data visualization.

Why This Book?

Clinical research presents unique challenges in data analysis. From stringent regulatory requirements to complex study designs, the tools we use need to be both powerful and transparent. R has emerged as a leading programming language in this field due to its flexibility, extensive package ecosystem, and strong community support.

This book provides:

• Practical techniques for working with clinical data in R
• Modern approaches using the tidyverse and other contemporary R packages
• Reproducible workflows that meet regulatory standards
• Visualizations designed specifically for clinical data communication

Who Should Read This Book?

This book is designed for:

• Clinical researchers and biostatisticians
• R programmers in healthcare and pharmaceutical industries
• Graduate students in biostatistics, epidemiology, and public health
• Healthcare professionals interested in data analysis

While we assume some familiarity with R basics, we’ve designed the content to be accessible to those who are transitioning from other tools (like SAS or SPSS) and to experienced R users looking to enhance their clinical research toolkit.

Book Structure

The book is organized into four main sections:

1. Fundamentals: Introduction to R in clinical contexts, data preparation, and exploratory analysis
2. Advanced Analysis: Statistical modeling, clinical trial design, and reproducible research practices
3. Visualization & Communication: Effective data visualization techniques and case studies
4. Interactive Elements: Shiny applications and interactive visualizations for enhanced learning

Code Examples

All code examples in this book are executable and available in the accompanying GitHub repository. Throughout the book, we emphasize the “vibe” of coding in a clinical context—that is, the mindset, practices, and approaches that make for effective and compliant analysis.

# Example code style used throughout the book
library(tidyverse)
library(survival)

# Load sample clinical data
clinical_data <- read_csv("data/sample_clinical_data.csv") %>%
  mutate(treatment = factor(treatment_group),
         event = as.logical(event_status))

# Example analysis
survival_model <- survfit(Surv(time, event) ~ treatment, 
                        data = clinical_data)

# Visualization with ggplot2
ggplot() + 
  geom_step(aes(x = survival_model$time, 
                y = survival_model$surv, 
                color = survival_model$strata)) +
  labs(title = "Kaplan-Meier Survival Curves",
       x = "Time",
       y = "Survival Probability",
       color = "Treatment Group") +
  theme_minimal()

Acknowledgments

We would like to thank the R community, especially those developing tools for clinical research, and the many reviewers who provided valuable feedback on early drafts of this book.

Let’s begin our journey into the world of clinical R programming!