clinicalsignificance

Conduct clinical significance analyses in clinical intervention studies.

https://github.com/benediktclaus/clinicalsignificance

Repository

Conduct clinical significance analyses in clinical intervention studies.

README.Rmd

---
output: 
  github_document:
    fig_height: 5
    fig_width: 7
bibliography: data-raw/references.bib
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
editor_options: 
  chunk_output_type: console
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  dpi = 320,
  fig.align = "center"
)

ggplot2::theme_set(ggplot2::theme_light())
```

# clinicalsignificance 


```{r, echo=FALSE, results='asis'}
cat(
  '[![CRAN status](https://img.shields.io/cran/v/clinicalsignificance.svg)](https://CRAN.R-project.org/package=clinicalsignificance)',
  '[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/clinicalsignificance)](https://cran.r-project.org/package=clinicalsignificance)',
  '[![R-CMD-check](https://github.com/pedscience/clinicalsignificance/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/pedscience/clinicalsignificance/actions/workflows/R-CMD-check.yaml)'
)
```


The **clinicalsignificance** R package provides a comprehensive and consistent framework for analyzing **clinical significance** in intervention studies. While *statistical* significance only indicates whether an effect is unlikely due to chance, clinical significance addresses the crucial question: Does an intervention lead to a **practically relevant or meaningful change** for the individual patient?

This package is designed to help researchers and healthcare professionals assess the practical relevance of their findings and make more informed decisions.

## Core Functions

The package implements the most common methods for clinical significance analysis, each accessible through its own main function:

* `cs_anchor()`: Evaluates change based on a predefined **minimal important difference (MID)**.
* `cs_percentage()`: Assesses the **percentage change** relative to the baseline score.
* `cs_distribution()`: Determines if a change is **statistically reliable** and exceeds measurement error (e.g., using the Reliable Change Index, RCI).
* `cs_statistical()`: Determines if a patient has moved from a **clinical to a functional population**.
* `cs_combined()`: Combines multiple approaches (e.g., distribution-based and statistical) for a more rigorous and nuanced assessment.

## Installation
You can install the stable version of **clinicalsignificance** from CRAN:

```{r, eval=FALSE}
install.packages("clinicalsignificance")
```

Or, install the development version from GitHub:

```{r, eval=FALSE}
# install.packages("pak")
pak::pak("benediktclaus/clinicalsignificance")
```

## Example: A Combined Approach

Let's demonstrate its use with the `Claus et al. (2020)` study included in the package. We will use the combined approach by Jacobson & Truax (1991), which assesses if a change is both reliable and crosses the cutoff into a functional population.

For this, we need descriptive data from a functional (non-clinical) population for the instrument used (BDI-II) and a reliability estimate.

```{r example, fig.height=5, fig.width=7}
library(clinicalsignificance)
library(ggplot2)

# Perform the analysis using the combined approach
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"
  )

# Display a summary of the results
summary(results_combined)

# Visualize the results
plot(results_combined, show_group = "category")
```

The plot shows each patient as a point. The categories are clearly visible:
- **Recovered**: Patients whose change was reliable AND who moved into the functional population range post-treatment.
- **Improved**: Patients whose change was reliable but who remained within the clinical range.
- **Unchanged**: Patients with no reliable change.
- **Deteriorated**: Patients with a reliable worsening of symptoms.

## Learn More

* **Vignettes**: For a detailed introduction to the different methods and their application, please see the package vignettes on the [**package website**](https://benediktclaus.github.io/clinicalsignificance/articles/).
* **Publication**: The package and its underlying methods are described in detail in the [**Journal of Statistical Software**](https://doi.org/10.18637/jss.v111.i01).


## Citation

If you use this package in your research, please cite both the package and the accompanying JSS paper.

```{r, eval=FALSE}
# You can get the citations directly in R
citation("clinicalsignificance")
```

**For the JSS paper:**

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. 

**BibTeX entries:**
```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/pedscience/clinicalsignificance/](https://github.com/pedscience/clinicalsignificance/)},
}
```


## Contributing
We welcome contributions from the community! If you find any bugs, have feature requests, or would like to contribute code, please open an [Issue](https://github.com/pedscience/clinicalsignificance/issues) or submit a Pull Request on GitHub.

## License
This package is released under the GNU General Public License v3.0. You are free to use and distribute it according to the terms of the license.

---

Thank you for using the **clinicalsignificance** R package! We hope it proves to be a valuable tool for your research. If you find it helpful, please consider giving us a star on [GitHub](https://github.com/pedscience/clinicalsignificance).

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