Description
Optimize PTSD Diagnostic Criteria.
Description
Provides tools for analyzing and optimizing PTSD (Post-Traumatic Stress Disorder) diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5) data. Functions identify optimal subsets of PCL-5 items that maintain diagnostic accuracy while reducing assessment burden. Includes tools for both hierarchical (cluster-based) and non-hierarchical symptom combinations, calculation of diagnostic metrics, and comparison with standard DSM-5 criteria. Model validation is conducted using holdout and cross-validation methods to assess robustness and generalizability of the results. For more details see Weidmann et al. (2025) <doi:10.31219/osf.io/6rk72_v1>.
README.md
PTSDdiag
Description
PTSDdiag is a comprehensive R package for analyzing and simplifying PTSD diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5) data. It provides tools to identify optimal subsets of six PCL-5 items that maintain diagnostic accuracy while reducing assessment burden.
Key Features
- Data preparation and standardization for PCL-5 scores
- Implementation of DSM-5 diagnostic criteria
- Calculation of diagnostic metrics and summary statistics
- Simplification of diagnostic criteria through:
- Hierarchical (cluster-based) approach
- Non-hierarchical approach
- Comparison of different diagnostic approaches
- Model validation using:
- Holdout Validation
- Cross-Validation
Installation
This package is currently only hosted on GitHub. It can be installed using the usual way:
install.packages("devtools")
devtools::install_github("WeidmannL/PTSDdiag")
Getting Started
The vignette demonstrates how to use the package to prepare the PCL-5 data, calculate some basic descriptive statistics and reliability metrics, find the optimal minimal symptom combinations for PTSD diagnosis, compare different diagnostic approaches and perform validation methods for evaluating model performance.
Bugs, Contributions
- If you have any suggestions or if you find a bug, please report them using GitHub issue tracker.
- Contributions are welcome! Please feel free to submit a Pull Request.