Welcome to the ClinicoPath Tutorial Series! These step-by-step guides teach you how to perform sophisticated clinical and pathological analyses using ClinicoPath for jamovi.
Target Audience: Clinicians, pathologists, and researchers with no programming experience required.
| # | Tutorial | Time | Difficulty | Topics |
|---|---|---|---|---|
| 1 | Getting Started | 30 min | ⭐ Beginner | Installation, navigation, first analysis |
| 2 | Table One for Clinical Trials | 45 min | ⭐⭐ Intermediate | Baseline characteristics, group comparisons, effect sizes |
| 3 | Survival Analysis in Oncology | 60 min | ⭐⭐ Intermediate | Kaplan-Meier, log-rank, Cox regression |
| 4 | ROC Analysis for Diagnostic Tests | 45 min | ⭐⭐ Intermediate | Sensitivity, specificity, AUC, cutpoints |
| 5 | Decision Curve Analysis | 50 min | ⭐⭐⭐ Advanced | Clinical utility, net benefit, threshold selection |
| 6 | Creating Reproducible Reports | 40 min | ⭐⭐ Intermediate | Quarto integration, automation, manuscripts |
Total Time: ~4.5 hours (can be completed over multiple sessions)
Tutorial 1 → Tutorial 2 → Tutorial 3 → Tutorial 6
Master descriptive statistics, group comparisons, survival analysis, and automated reporting.
Tutorial 1 → Tutorial 4 → Tutorial 5
Focus on diagnostic test evaluation, ROC curves, and clinical decision-making.
Tutorial 1 → 2 → 3 → 4 → 5 → 6 (complete series)
Master all aspects of clinicopathological research analysis.
Goal: Install ClinicoPath and run your first analysis
Topics:
- Installing jamovi and ClinicoPath modules
- Navigating the ClinicoPath interface
- Importing clinical data (CSV, Excel, SPSS)
- Creating a basic Table One
- Interpreting results
- Exporting publication-ready tables
Dataset: clinical_trial_data (n=200)
Key Skills: ✅ jamovi installation ✅ Data import ✅ Table One generation ✅ Result interpretation
Goal: Create publication-quality baseline characteristics tables with statistical comparisons
Topics:
- Stratified analysis by treatment group
- Automatic statistical test selection (t-test, chi-square, Fisher's exact)
- Effect sizes (Cohen's d, Cramér's V)
- Multiple testing corrections (Bonferroni, Holm, FDR)
- Missing data handling and reporting
- NEJM, Lancet, and gtsummary table styles
Dataset: clinical_trial_data with group comparisons
Key Skills: ✅ Stratified baseline tables ✅ Statistical tests for group comparisons ✅ Effect size calculation and interpretation ✅ Manuscript-ready Methods and Results sections
Goal: Perform comprehensive survival analysis for cancer research
Topics:
- Kaplan-Meier survival curves with confidence intervals
- Log-rank, Wilcoxon, and Tarone-Ware tests
- Median survival time calculation
- 1-year, 3-year, and 5-year survival rates
- Cox proportional hazards regression (univariate and multivariate)
- Hazard ratios with forest plots
- Proportional hazards assumption testing (Schoenfeld residuals)
- C-index for model discrimination
Dataset: basic_survival_data (breast cancer, n=200)
Key Skills: ✅ Kaplan-Meier analysis ✅ Log-rank tests ✅ Cox regression ✅ Hazard ratio interpretation ✅ Forest plots for publication
Goal: Evaluate diagnostic test performance using ROC curves
Topics:
- Receiver Operating Characteristic (ROC) curves
- Area Under the Curve (AUC) with 95% CI
- Sensitivity and specificity at multiple cutpoints
- Positive and negative predictive values (PPV, NPV)
- Optimal cutpoint determination (Youden index, cost-benefit)
- Comparing multiple biomarkers
- Time-dependent ROC for survival outcomes
- Grey zone analysis
Dataset: diagnostic_biomarker_data (HER2 testing, n=150)
Key Skills: ✅ ROC curve generation ✅ AUC interpretation ✅ Cutpoint optimization ✅ Comparing diagnostic tests ✅ Clinical threshold selection
Goal: Assess clinical utility and net benefit of prediction models
Topics:
- Decision curve analysis (DCA)
- Net benefit calculation
- Clinical utility vs. statistical significance
- Threshold probability selection
- Comparing prediction models
- Interventions avoided calculation
- Time-dependent DCA for survival
- Integration with ROC analysis
Dataset: risk_prediction_model_data (breast cancer recurrence, n=300)
Key Skills: ✅ Decision curve generation ✅ Net benefit interpretation ✅ Clinical threshold selection ✅ Model comparison ✅ Communicating clinical utility
Goal: Automate analyses and generate reproducible manuscripts
Topics:
- Using ClinicoPath programmatically in R
- Quarto document integration
- Automated Table One, survival curves, ROC plots
- Batch processing multiple datasets
- Version control with Git
- Reproducible workflow best practices
- Manuscript templates for common study designs
Dataset: Multiple example datasets
Key Skills: ✅ R scripting with ClinicoPath ✅ Quarto report generation ✅ Workflow automation ✅ Reproducible research practices
- jamovi ≥ 2.5 (free download: jamovi.org)
- ClinicoPath modules (install via jamovi library)
- Computer: Windows 10+, macOS 10.14+, or Linux Ubuntu 18.04+
- RAM: 4 GB minimum, 8 GB recommended
- Disk Space: 1 GB for jamovi + ClinicoPath + example data
- Tutorial 1: None! Complete beginners welcome.
- Tutorials 2-6: Completion of Tutorial 1 recommended.
- No programming experience required for any tutorial.
- R ≥ 4.1 (for programmatic use)
- RStudio (recommended IDE)
- Quarto (for report generation)
All tutorials use realistic clinical datasets included with ClinicoPath:
| Dataset | N | Description | Used in Tutorials |
|---|---|---|---|
clinical_trial_data |
200 | Randomized breast cancer trial | 1, 2 |
basic_survival_data |
200 | Breast cancer survival cohort | 3 |
diagnostic_biomarker_data |
150 | HER2 testing validation | 4 |
risk_prediction_model_data |
300 | Recurrence prediction | 5 |
lung_agreement_data |
100 | Pathologist agreement study | -- |
her2_breast_cancer_data |
150 | HER2-low classification | -- |
All datasets are de-identified and free to use for learning and teaching.
Each tutorial includes:
✅ Learning objectives - What you'll master ✅ Clinical scenario - Realistic research question ✅ Step-by-step walkthrough - Click-by-click instructions with screenshots ✅ Result interpretation - How to read and understand outputs ✅ Common mistakes - Pitfalls to avoid ✅ Manuscript reporting - Methods and Results text templates ✅ Practice exercises - Apply skills to new datasets ✅ Summary - Key takeaways and next steps
- Start with Tutorial 1 (essential for all users)
- Follow your learning path based on research focus
- Complete practice exercises at the end of each tutorial
- Apply skills to your own data
- Each tutorial = one 90-minute lab session
- Students work through tutorials at their own pace
- Instructor provides guidance on practice exercises
- Final project: Analyze real dataset using learned skills
- Assign tutorials as onboarding for new team members
- Use as reference for specific analyses
- Standardize analytical approaches across team
- Share custom tutorials for lab-specific workflows
- ClinicoPath Website: www.serdarbalci.com/ClinicoPathJamoviModule
- Development Guides: vignettes/ (for developers)
- Function Reference: man/ (R documentation)
- GitHub Issues: Report bugs or request features
- jamovi Forum: Ask questions
- Email Support: serdarbalci@serdarbalci.com
These tutorials align with the scientific-skills framework for:
- peer-review: Critical evaluation of statistical methods
- statistical-analysis: Rigorous hypothesis testing
- clinical-decision-support: Evidence-based medicine
- scientific-writing: Manuscript preparation
If you use these tutorials in your teaching or research, please cite:
Balci, S. (2025). ClinicoPath Tutorial Series: Step-by-Step Guides for
Clinicopathological Research. ClinicoPath Jamovi Module.
https://www.serdarbalci.com/ClinicoPathJamoviModule/tutorials/
BibTeX:
@Misc{clinicopath-tutorials2025,
title = {ClinicoPath Tutorial Series: Step-by-Step Guides for Clinicopathological Research},
author = {Serdar Balci},
year = {2025},
url = {https://www.serdarbalci.com/ClinicoPathJamoviModule/tutorials/},
}Have suggestions for improving tutorials or ideas for new ones?
- Open an issue: GitHub Issues
- Submit corrections: Fork → Edit → Pull Request
- Share use cases: Email examples of how you used tutorials
- Request topics: What analyses do you need tutorials for?
Tutorials: CC-BY-4.0 (Creative Commons Attribution 4.0 International) Software (ClinicoPath): GPL-2
You are free to:
- ✅ Share tutorials with students and colleagues
- ✅ Adapt tutorials for your courses
- ✅ Translate tutorials into other languages
- ✅ Use tutorial examples in your research
Attribution: Please cite ClinicoPath and provide a link to these tutorials.
These tutorials were developed using:
- jamovi framework: The jamovi project (2025)
- Clinical examples: Based on published research and simulated data
- Statistical methods: Standard clinicopathological research practices
- Peer review: Aligned with scientific peer-review standards
Special thanks to the jamovi community and ClinicoPath users who provided feedback on early drafts.
| Tutorial | Status | Last Updated |
|---|---|---|
| Tutorial 1: Getting Started | ✅ Complete | Dec 13, 2025 |
| Tutorial 2: Table One | ✅ Complete | Dec 13, 2025 |
| Tutorial 3: Survival Analysis | ✅ Complete | Dec 13, 2025 |
| Tutorial 4: ROC Analysis | 🚧 In Progress | -- |
| Tutorial 5: Decision Curve Analysis | 🚧 In Progress | -- |
| Tutorial 6: Reproducible Reports | 🚧 In Progress | -- |
Ready to start? Begin with Tutorial 1: Getting Started →
Questions? Check the FAQ or contact us.
Q: Do I need to complete all tutorials? A: No! Choose the learning path that matches your research focus. Tutorial 1 is recommended for everyone.
Q: Can I skip Tutorial 1 if I already use jamovi? A: We recommend scanning Tutorial 1 to learn ClinicoPath-specific features and navigation.
Q: How long does each tutorial take? A: 30-60 minutes per tutorial, but you can work at your own pace. Tutorials are designed to be completed in one sitting or split across multiple sessions.
Q: Do tutorials work with my own data? A: Yes! After completing tutorials with example data, apply the same methods to your datasets. Ensure your data follows the format guidelines in Tutorial 1.
Q: Can I use tutorials for teaching? A: Absolutely! Tutorials are licensed CC-BY-4.0 and designed for classroom use. Adapt as needed for your courses.
Q: Are video tutorials available? A: Not yet, but we're working on video supplements. Written tutorials with screenshots provide comprehensive guidance.
Q: What if I get stuck? A: Each tutorial has a Troubleshooting section. For additional help, visit the jamovi forum or email us.
Tutorial Author: Serdar Balci, MD, PhD Email: serdarbalci@serdarbalci.com Website: www.serdarbalci.com GitHub: sbalci/ClinicoPathJamoviModule
Last Updated: December 13, 2025 Tutorial Series Version: 1.0 Compatible with: ClinicoPath ≥ 0.0.32, jamovi ≥ 2.5