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clinicalsignificance

The clinicalsignificance R package provides a comprehensive toolkit for analyzing clinical significance in intervention studies.

Why this package? While statistical significance asks: “Is this effect unlikely due to chance?” Clinical significance asks: “Does this intervention make a meaningful difference for the patient?”

This package empowers researchers and practitioners to move beyond p-values and assess the practical relevance of treatment outcomes.

🛠 Core Functions

The package implements the most widely used methods for clinical significance analysis. Each approach answers a specific question:

cs_anchor(): Did the patient improve by a minimal amount? Evaluates change based on a predefined Minimal Important Difference (MID).
cs_percentage(): Did the patient improve by a certain percentage? Assesses change relative to the baseline score.
cs_distribution(): Is the change reliable (beyond measurement error)? Uses statistical distribution metrics like the Reliable Change Index (RCI).
cs_statistical(): Did the patient return to a functional range? Determines if a patient moved from a clinical to a functional population.
cs_combined(): The “Gold Standard” (e.g., Jacobson & Truax). Combines reliability and cutoff criteria for a robust assessment.

📦 Installation

Install the stable version from CRAN:

install.packages("clinicalsignificance")

Or the development version from GitHub:

# install.packages("pak")
pak::pak("benediktclaus/clinicalsignificance")

🚀 Example: The Combined Approach

Let’s look at the combined approach (Jacobson & Truax, 1991). We want to know if patients in the claus_2020 dataset (included in the package) showed a reliable change AND moved into a functional population range.

library(clinicalsignificance)
library(ggplot2)

# 1. Perform the analysis
results_combined <- claus_2020 |>
  cs_combined(
    id = id,
    time = time,
    outcome = bdi,
    pre = 1,
    post = 4,
    reliability = 0.801,
    m_functional = 7.69,
    sd_functional = 7.52,
    cutoff_type = "c"
  )

# 2. Visualize the results
plot(results_combined, show_group = "category")
#> Ignoring unknown labels:
#> • colour : "Group"

Interpreting the Plot: * Recovered (Green): Reliable improvement + moved to functional range. * Improved (Blue): Reliable improvement, but still in clinical range. * Unchanged (Grey): No reliable change. * Deteriorated (Red): Reliable worsening.

# 3. Get a summary table
summary(results_combined)
#> 
#> ---- Clinical Significance Results ----
#> 
#> Approach:     Distribution-based
#> RCI Method:   JT
#> N (original): 43
#> N (used):     40
#> Percent used: 93.02%
#> Outcome:      bdi
#> Cutoff Type:  c
#> Cutoff:       21.02
#> Outcome:      bdi
#> Reliability:  0.801
#> 
#> -- Cutoff Descriptives
#> 
#> M Clinical | SD Clinical | M Functional | SD Functional
#> -------------------------------------------------------
#> 35.48      |        8.16 |         7.69 |          7.52
#> 
#> 
#> -- Results
#> 
#> Category     |  N | Percent
#> ---------------------------
#> Recovered    | 10 |  25.00%
#> Improved     |  8 |  20.00%
#> Unchanged    | 22 |  55.00%
#> Deteriorated |  0 |   0.00%
#> Harmed       |  0 |   0.00%

📚 Learn More

Vignettes: Detailed tutorials on all methods are available on the package website.
Publication: For the theoretical background, check out our paper in the Journal of Statistical Software.

📄 Citation

Please cite both the package and the JSS paper if you use clinicalsignificance in your research.

Claus, B. B., Wager, J., & Bonnet, U. (2024). clinicalsignificance: Clinical Significance Analyses of Intervention Studies in R. Journal of Statistical Software, 111(1), 1–39. https://doi.org/10.18637/jss.v111.i01

Click to show BibTeX

@article{JSS:v111:i01,
  author = {Benedikt B. Claus and Julia Wager and Udo Bonnet},
  title = {{clinicalsignificance}: Clinical Significance Analyses of Intervention Studies in {R}},
  journal = {Journal of Statistical Software},
  year = {2024},
  volume = {111},
  number = {1},
  pages = {1--39},
  doi = {10.18637/jss.v111.i01},
}

@manual{R-clinicalsignificance,
  title = {clinicalsignificance: A Toolbox for Clinical Significance Analyses in Intervention Studies},
  author = {Benedikt B. Claus},
  year = {2024},
  note = {R package version 2.1.0},
  doi = {10.32614/CRAN.package.clinicalsignificance},
  url = {[https://github.com/benediktclaus/clinicalsignificance/](https://github.com/benediktclaus/clinicalsignificance/)},
}

🤝 Contributing

Contributions are welcome! If you encounter bugs or have feature requests: 1. Check the Issue Tracker. 2. Submit a Pull Request.

License: GNU General Public License v3.0
Built with ❤️ for better clinical research.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.