ClinKit: Streamlined Clinical Data Analysis in R

Introduction

ClinKit is an integrated R package designed to transform cleaned clinical datasets directly into publication-ready figures and statistical outputs, while preserving access to raw analysis objects for complete methodological transparency and customization. It is tailored for clinical research workflows, providing a one-stop solution for routine statistical analysis and reporting.

Installation

You can install the development version of ClinKit from GitHub:

# install.packages("devtools")
devtools::install_github("LeafLight/ClinKit")

Example Data

For demonstration, we will use a simulated dataset:

library(ClinKit)
set.seed(123)
data <- data.frame(
  group = rep(c("A", "B"), each = 50),
  age = rnorm(100, 60, 10),
  gender = sample(c("Male", "Female"), 100, replace = TRUE),
  outcome = rbinom(100, 1, 0.4)
)
head(data)

Baseline Table Generation

Generate a baseline characteristics table with automatic test selection:

result <- make_baseline_table(data, group_var = "group")
print(result$summary)
# Export to Word
gtsummary::as_flextable(result$summary) |> flextable::save_as_docx(path = "baseline_table.docx")

Regression Analysis

Univariate Logistic Regression

uni_res <- run_univariate_logistic_regression(data, outcome = "outcome", predictors = c("age", "gender"))
print(uni_res$summary)

Multivariable Logistic Regression

multi_res <- run_multivariable_logistic_regression(data, outcome = "outcome", predictors = c("age", "gender"), covariates = c("group"))
print(multi_res$summary)

Visualization

Scatter Plot with Linear Regression

scatter_lm(data, x = "age", y = "outcome")

Forest Plot

# Example: subgroup_forest
# (Assume you have a suitable regression result)
# subgroup_forest(model_result, subgroup_var = "gender")

Specialized Analyses

High/Low Group Analysis

# highlow_analysis(data, outcome = "outcome", exposure1 = "age", exposure2 = "group")

Quartile-Based Analysis

# quartile_logistic_analysis(data, outcome = "outcome", predictor = "age")

ROC Curve Analysis

# roc_analysis(data, outcome = "outcome", predictors = c("age", "group"))

Exporting Results

Tables can be exported to Word (.docx) or CSV.
Figures can be saved as PNG, PDF, or SVG.

Advanced Usage

All functions return raw analysis objects for further customization.
Supports integration with tidyverse workflows.
Publication-ready outputs with minimal code.

Conclusion

ClinKit streamlines the process of clinical data analysis and reporting in R, making it accessible, reproducible, and publication-ready. For more details, visit the ClinKit documentation website.

Hualin Wang

2025-10-15