clinicopathjamovimodule

ClinicoPath jamovi Module

https://github.com/sbalci/clinicopathjamovimodule

Science Score: 49.0%

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Low similarity (17.7%) to scientific vocabulary

Keywords

correlation decision jamovi module natural-language-summaries r-package report statistics summary survival survival-analysis tableone

Last synced: 4 months ago · JSON representation

Repository

ClinicoPath jamovi Module

Basic Info

Host: GitHub
Owner: sbalci
License: gpl-2.0
Language: R
Default Branch: master
Homepage: https://www.serdarbalci.com/ClinicoPathJamoviModule
Size: 436 MB

Statistics

Stars: 20
Watchers: 5
Forks: 4
Open Issues: 0
Releases: 15

Topics

correlation decision jamovi module natural-language-summaries r-package report statistics summary survival survival-analysis tableone

Created about 6 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing Funding License Citation Codemeta Agents

README.Rmd

---
output: github_document
---



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

# ClinicoPath

---

## Abstract

### **The ClinicoPathJamoviModule: A Comprehensive Open-Source Toolkit for Streamlining Clinicopathological Research**

**Background:** Clinicopathological research is fundamental to advancing evidence-based medicine, but it requires the application of complex and specialized statistical methods. The technical barrier of programming-based statistical software can limit the ability of clinicians and researchers to perform these analyses efficiently and reproducibly. To address this gap, we have developed the ClinicoPathJamoviModule, an open-source extension for the user-friendly jamovi statistical platform.

**Methods:** The ClinicoPathJamoviModule integrates a robust and comprehensive suite of analytical tools designed to address the typical workflow of clinicopathological data analysis. The module is built upon the R statistical language and provides a graphical user interface within jamovi for a wide range of functions. Key features include:

*   **Descriptive Statistics:** Generation of publication-ready summary tables (`Table 1`).
*   **Survival Analysis:** A comprehensive framework including:
    *   **Core Methods:** Kaplan-Meier, Nelson-Aalen, and Turnbull NPMLE for interval-censored data.
    *   **Advanced Modeling:** Cox PH, time-varying effects, AFT models, and frailty models.
    *   **Specialized Applications:** Competing risks, multi-state models, recurrent event analysis, and cure models.
    *   **Machine Learning Integration:** Survival trees, random forests, gradient boosting, and regularized regression (LASSO, Ridge).
    *   **Oncological Analysis:** Specialized methods for Progression-Free Survival (PFS), treatment switching (IPCW), and tumor growth kinetics.
*   **Clinical Trial Design & Evaluation:**
    *   **Modern Designs:** Group sequential, adaptive, platform, and master (umbrella, basket) protocols.
    *   **Epidemiological Tools:** Population-based survival analysis, case-cohort designs, and attributable risk calculation.
*   **Diagnostic & Prognostic Accuracy:**
    *   **Test Evaluation:** ROC analysis, decision curve analysis (DCA), and screening test calculators.
    *   **Model Validation:** Time-dependent ROC, prediction error curves, C-statistics, and calibration plots.
*   **Agreement and Reliability:** 
    *   **Inter-rater Statistics:** Cohen's Kappa, Fleiss' Kappa, Krippendorff's Alpha, and ICC
    *   **Consensus Scoring:** Automated consensus determination with majority rule, super-majority, and unanimous methods
    *   **Digital Pathology Validation:** Modality comparison for glass slides vs digital images with HER2-specific analysis
    *   **Method Comparison:** Bland-Altman plots and Deming Regression
*   **Evidence-Based Decision Support:** A suite of tools for:
    *   **Clinical Monitoring:** Alert systems and patient monitoring dashboards.
    *   **Treatment Planning:** Optimization frameworks and differential diagnosis assistance.
    *   **Results Interpretation:** Laboratory and imaging findings correlation.
*   **Laboratory Quality Control:** ISO 15189 compliant statistical process control, including Shewhart/CUSUM charts, Six Sigma metrics, and measurement uncertainty.
*   **Spatial Statistics & Digital Pathology:** 
    *   **Spatial Analysis:** Spatial point pattern analysis (Ripley's K-function), spatial autocorrelation (Moran's I)
    *   **Texture Analysis:** Haralick texture features for quantitative pathology
    *   **Validation Studies:** Glass slide vs digital image comparison with discordance pattern analysis
    *   **HER2 Scoring:** Specialized tools for HER2-low/null distinction and ADC therapy eligibility
*   **Data Visualization:** Advanced plotting tools such as waterfall plots, swimmer plots, and raincloud plots.
*   **Data Preprocessing:** Utilities for data quality checking, missing data management, and date corrections.

**Conclusion:** The ClinicoPathJamoviModule provides a powerful, accessible, and free-to-use toolkit that empowers medical researchers to conduct sophisticated statistical analyses without requiring extensive programming knowledge. By integrating these essential functions into the intuitive jamovi framework, the module aims to lower the barrier to high-quality data analysis, enhance research reproducibility, and accelerate the translation of clinical data into meaningful insights.

---

## Documentation

For a comprehensive guide to all the documentation available for this package, please see the [Documentation Hub](vignettes/documentation_hub.md).

---


## ClinicoPath [jamovi](https://www.jamovi.org) Module 

A [jamovi](https://www.jamovi.org) Module that contains main analysis used in **ClinicoPathological** research. ClinicoPath help researchers to generate natural language summaries of their dataset, generate cross tables with statistical tests, and survival analysis with survival tables, survival curves, and natural language summaries.











---

[![Download ClinicoPathJamoviModule](https://a.fsdn.com/con/app/sf-download-button)](https://sourceforge.net/projects/clinicopathjamovimodule/files/latest/download)


https://zenodo.org/account/settings/github/repository/sbalci/ClinicoPathJamoviModule

DOI 10.17605/OSF.IO/9SZUD

https://osf.io/9szud/


---

## Installation in [jamovi](https://www.jamovi.org)


You can install this module after installing jamovi version >= 2.7.2 from here: https://www.jamovi.org/download.html  

Then you can install the submodules directly inside the jamovi, using library.  

Submodules are:

- ClinicoPathDescriptives  
- jsurvival  
- meddecide  
- jjstatsplot  



  

  




## Installation via sideload [jamovi](https://www.jamovi.org)


### Step 1:

**Download and install [jamovi](https://www.jamovi.org).**  


### Step 2:

**Download the relevant `jmo` file for your operating system from**  

#### a: For development version


- [releases](https://github.com/sbalci/ClinicoPathJamoviModule/releases/)



#### b: For modules


##### ClinicoPathDescriptives

ClinicoPathDescriptives functions are separately added to jamovi library under Exploration menu


ClinicoPathDescriptives module can be downloaded inside jamovi (click Modules and jamovi library)


https://github.com/sbalci/ClinicoPathDescriptives/

https://github.com/sbalci/ClinicoPathDescriptives/releases/






```r
remotes::install_github("sbalci/ClinicoPathDescriptives")
```










##### JJStatsPlot  

GGStatsPlot functions are separately added to jamovi library under jjstatsplot menu  

JJStastPlot module can be downloaded inside jamovi (click Modules and jamovi library)  









https://github.com/sbalci/jjstatsplot  

https://github.com/sbalci/jjstatsplot/releases/  


```r
remotes::install_github("sbalci/jjstatsplot")
```













##### jsurvival

https://github.com/sbalci/jsurvival

https://github.com/sbalci/jsurvival/releases/








```r
remotes::install_github("sbalci/jsurvival")
```













##### meddecide  

https://github.com/sbalci/meddecide/  

https://github.com/sbalci/meddecide/releases/  








```r
remotes::install_github("sbalci/meddecide")
```
























**Step 3: And install using side-load as shown below:**





---


## Screenshots of Module

---

### Example Datasets

**Using Example Datasets**



---


https://cloud.jamovi.org/?open=https://raw.githubusercontent.com/sbalci/ClinicoPathJamoviModule/master/data/histopathology.csv


---


https://cloud.jamovi.org/?open=https://raw.githubusercontent.com/sbalci/ClinicoPathJamoviModule/master/data/histopathology.omv



https://cloud.jamovi.org/?open=https://docs.google.com/spreadsheets/d/e/2PACX-1vST3kwze9bNUSEr0eijs_81F6hXBrDZ-2Zt97ez-fbpXMELKGFHJNuQHSP2Oxars2C6F3n50KzT1-zD/pub?output=csv




### Exploration

#### ClinicoPath Descriptives

##### [Age Pyramid](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/agepyramid_documentation.html)


##### [Age Pyramid 2](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/agepyramid2_documentation.html)
(Similar to Age Pyramid with more styling options)

##### [Alluvial Diagrams](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/alluvial_documentation.html)


##### [Alluvial Diagrams 2](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/alluvial2_documentation.html)
(Similar to Alluvial Diagrams with more styling options)

##### [Missing Data Analysis and Imputation](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/missingdata_documentation.html)
(Comprehensive missing data analysis and multiple imputation)

##### [Outlier Detection](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/outlierdetection_documentation.html)
(Advanced outlier detection with multiple statistical methods)

##### [Summary of Categorical Variables](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/reportcat_documentation.html)


##### [Summary of Categorical Variables 2](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/reportcat2_documentation.html)
(Enhanced categorical summary)

##### [Raincloud Plot](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/raincloud_documentation.html)
(Comprehensive distribution analysis)

##### [Summary of Continuous Variables](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/summarydata_documentation.html)


##### [Summary of Continuous Variables 2](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/summarydata2_documentation.html)
(Enhanced summary statistics for continuous and date variables)

##### [Swimmer Plot](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/swimmerplot_documentation.html)
(Comprehensive swimmer plots for visualizing patient timelines)

##### [Table One](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/tableone_documentation.html)



##### [Table One 2](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/tableone2_documentation.html)
(Enhanced Table One with pivottabler)

##### [Tidy Density](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/tidydensity_documentation.html)
(Statistical distribution generator and analyzer)

##### [Tools Summary](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/toolssummary_documentation.html)
(Tools for data summary with summarytools integration)

##### [Toxicity Profile](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/toxicityprofile_documentation.html)
(Treatment toxicity profile analysis)

##### [Venn Diagram](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/venn_documentation.html)
(Venn and Upset diagrams)

##### [Variable Tree](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/vartree_documentation.html)
  
  

##### [Waterfall Plot](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/waterfall_documentation.html)
(Treatment response analysis)

---

#### ClinicoPath Comparisons

##### Cross Tables











##### Pairwise Chi-Square Tests

 UNDER CONSTRUCTION 


### JJStatsPlot

#### Graphs and Plots

##### [Advanced Barplot](https://www.serdarbalci.com/jjstatsplot/articles/advancedbarplot_documentation.html)
(Advanced bar charts - 5 ways)

##### [Advanced Raincloud Plot](https://www.serdarbalci.com/jjstatsplot/articles/advancedraincloud_documentation.html)
(Advanced raincloud plot with longitudinal support)

##### [Predictive Power Score Analysis](https://www.serdarbalci.com/jjstatsplot/articles/jpps_documentation.html)
(Predictive Power Score Analysis)

##### [High-Performance Scatter Plots](https://www.serdarbalci.com/jjstatsplot/articles/jscattermore_documentation.html)
(Fast scatter plots for large datasets)

##### [Social Science Statistical Visualization](https://www.serdarbalci.com/jjstatsplot/articles/jsjplot_documentation.html)
(sjPlot Integration for Social Science Research)

##### [Professional Violin Plot](https://www.serdarbalci.com/jjstatsplot/articles/jviolin_documentation.html)
(Advanced Violin Plots for Data Distribution)

##### [Waffle Charts](https://www.serdarbalci.com/jjstatsplot/articles/jwaffle_documentation.html)
(Create Waffle Charts to visualize distributions)

##### [Line Chart](https://www.serdarbalci.com/jjstatsplot/articles/linechart_documentation.html)
(Line Chart for Time Series and Trend Analysis)

##### [Lasso-Cox Regression](https://www.serdarbalci.com/jjstatsplot/articles/lassocox_documentation.html)
(Lasso-Cox Regression for Variable Selection in Survival Analysis)

##### [Parallel Coordinates Plot](https://www.serdarbalci.com/jjstatsplot/articles/parallelplot_documentation.html)
(Multivariate Exploration)

##### [Automatic Plot Selection](https://www.serdarbalci.com/jjstatsplot/articles/statsplot2_documentation.html)
(Automatic Plot Selection Based on Variable Types)

##### [Statistical Distribution Generator](https://www.serdarbalci.com/jjstatsplot/articles/tidydensity_documentation.html)
(TidyDensity - Distribution Analysis & Simulation)

##### [Within-Group Comparison](https://www.serdarbalci.com/jjstatsplot/articles/jjwithinstats_documentation.html)
(Violin Plots to Compare Within Groups)

##### [Treemap](https://www.serdarbalci.com/jjstatsplot/articles/jjtreemap_documentation.html)
(Creates treemap visualizations for categorical data)

##### [StreamGraphs](https://www.serdarbalci.com/jjstatsplot/articles/jjstreamgraph_documentation.html)
(Create interactive StreamGraphs using R streamgraph package.)

##### [Scatter Plot](https://www.serdarbalci.com/jjstatsplot/articles/jjscatterstats_documentation.html)
(Scatter Plot for Continuous Variables)










---

### Survival

#### jsurvival

##### [Alluvial Survival Plot](https://www.serdarbalci.com/jsurvival/articles/alluvialsurvival_documentation.html)
(Treatment Pathway Alluvial Plot)

##### [Multivariable Survival Analysis](https://www.serdarbalci.com/jsurvival/articles/multisurvival_documentation.html)




##### [Odds Ratio Table and Plot](https://www.serdarbalci.com/jsurvival/articles/oddsratio_documentation.html)


##### [Kaplan-Meier Survival Analysis for Single Group](https://www.serdarbalci.com/jsurvival/articles/onesurvival_documentation.html)
(Kaplan-Meier Survival Analysis for Single Group)

##### [Single Arm Survival](https://www.serdarbalci.com/jsurvival/articles/singlearm_documentation.html)
(Survival for whole group, no explanatory factor)

##### [Comprehensive Survival Analysis](https://www.serdarbalci.com/jsurvival/articles/survival_documentation.html)





##### [Survival Analysis for Continuous Explanatory Variable](https://www.serdarbalci.com/jsurvival/articles/survivalcont_documentation.html)
(Cut-off & Univariate Survival Analysis)

##### [Survival Analysis Power & Sample Size](https://www.serdarbalci.com/jsurvival/articles/survivalpower_documentation.html)
(Power Analysis for Survival Studies)

##### [Time-Dependent ROC Analysis](https://www.serdarbalci.com/jsurvival/articles/timeroc_documentation.html)
(Predictive Performance Over Time)

##### [Comprehensive Time Interval Calculator](https://www.serdarbalci.com/jsurvival/articles/timeinterval_documentation.html)
(Advanced time interval analysis with quality assessment)

##### [Jvisr](https://www.serdarbalci.com/jsurvival/articles/jvisr_documentation.html)


(Fit-for-Purpose Clinical Visualizations)

##### [Competing Survival](https://www.serdarbalci.com/jsurvival/articles/15-competing-survival.html)


 UNDER CONSTRUCTION 



---

### meddecide

#### Agreement


##### Interrater Reliability





##### ICC coefficients

 UNDER CONSTRUCTION 


---

#### Decision

##### Medical Decision





##### [Decision Calculator](https://www.serdarbalci.com/meddecide/articles/35-screening-calculator-comprehensive.html)










##### [Decision Tree](https://www.serdarbalci.com/meddecide/articles/05-decision-tree-analysis.html)



##### [Decision Tree explore](https://www.serdarbalci.com/meddecide/articles/05-decision-tree-analysis.html)



 UNDER CONSTRUCTION 







###### FFTrees 

 UNDER CONSTRUCTION 






###### rpart

 UNDER CONSTRUCTION 





##### [ROC Analysis](https://www.serdarbalci.com/meddecide/articles/03-roc-analysis.html)


 UNDER CONSTRUCTION 


---

### Correlation

#### [Correlation](https://www.serdarbalci.com/ClinicoPathDescriptives/articles/01-correlation-analysis.html)






---




## Installation in R







You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("sbalci/ClinicoPathJamoviModule")
```


## Current Package Versions:

**R:** 4.0.5

**MRAN:** https://cran.microsoft.com/snapshot/2020-08-24


## Acknowledgement {#acknowledgement}

Made possible via the codes, help, and guidence of

- [jamovi](https://www.jamovi.org/) developer [Jonathon Love](https://github.com/jonathon-love),
- [finalfit](https://finalfit.org/) developer [Ewen Harrison](https://github.com/ewenharrison), 
- [ggstatsplot](https://indrajeetpatil.github.io/ggstatsplot/) developer [Indrajeet Patil](https://github.com/IndrajeetPatil), 
- [tangram](https://github.com/spgarbet/tangram) developer [Shawn Garbett](https://github.com/spgarbet), 
- [easystats](https://easystats.github.io/blog/) developers , 
- [report](https://easystats.github.io/report/) , 
- [tableone](https://github.com/kaz-yos/tableone), 
- [survival](https://github.com/therneau/survival), 
- [survminer](https://github.com/kassambara/survminer),
- [vtree](https://github.com/nbarrowman/vtree) developer [Nick Barrowman](https://github.com/nbarrowman),
- [easyalluvial](https://github.com/erblast/easyalluvial), developer [Bjrn Oettinghaus](https://github.com/erblast), and
- [rstats community](https://twitter.com/search?q=%23rstats&src=savs).


See https://github.com/ClinicoPath for forked packages.


---

Follow @serdarbalci  

Buy me a coffee  

https://paypal.me/serdarbalci


---

  
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## Videos



## Accessing Raw and External Data Files

Beyond the datasets readily available with `data(dataset_name)`, this package also includes various raw and external data files in other formats like CSV (Comma Separated Values), XLSX (Excel), and OMV (Jamovi files). These can be useful for users who want to access the original data, use it with other software, or understand how the R data objects (`.rda` files) were generated.

These files are typically located in the `inst/extdata` directory of the package. You can get the full path to a file in `inst/extdata` using the `system.file()` function. For example:

```r
# Get the path to 'BreastCancer.csv' in inst/extdata
# (Assuming BreastCancer.csv will be moved to inst/extdata in a later step)
csv_path <- system.file("extdata", "BreastCancer.csv", package = "ClinicoPath")

if (nzchar(csv_path)) {
  # Read the CSV file
  breast_cancer_df <- read.csv(csv_path)
  head(breast_cancer_df)
} else {
  message("BreastCancer.csv not found in inst/extdata. This example assumes it's present there.")
}
```

### Common File Types

#### CSV Files

Many datasets are available in CSV format. These can be easily read into R using `read.csv()` or other functions from packages like `readr` or `data.table`.

*   **Example:** The `BreastCancer` dataset, also available via `data(BreastCancer)`, has its source data potentially available as `BreastCancer.csv`.
*   Other CSV files like `oncology_response_data.csv` (related to the `treatmentResponse` dataset) and `colon.csv` are also available.

#### XLSX Files (Excel)

Some datasets might be provided in Excel format. You can read these using packages like `readxl`.

*   **Example:** `heartdisease.xlsx`
    ```r
    # Ensure readxl is installed: install.packages("readxl")
    # xlsx_path <- system.file("extdata", "heartdisease.xlsx", package = "ClinicoPath")
    # if (nzchar(xlsx_path)) {
    #   heartdisease_df <- readxl::read_excel(xlsx_path)
    #   head(heartdisease_df)
    # }
    ```
    *(Note: The availability and specific location of `heartdisease.xlsx` in `inst/extdata` will be finalized in a later step).*

#### OMV Files (Jamovi)

Files with the `.omv` extension are project files for Jamovi, a free and open statistical spreadsheet. These files often contain datasets and analyses demonstrating the use of this R package's functionalities within the Jamovi environment. They are not typically read directly into R but opened with Jamovi.

*   **Examples:** `BreastCancer.omv`, `colon.omv`, `histopathology.omv`, and many others found in `data/` or `inst/extdata/`.

#### JASP Files

Files with the `.jasp` extension are for JASP software, another alternative to SPSS. Similar to Jamovi files, these demonstrate analyses and data.

*   **Example:** `histopathology_jasp.jasp`

### Relationship to `.rda` Data Files

Many of the `.rda` files (loaded using `data(dataset_name)`) provided by this package are derived from these raw data files (like CSVs). The `.rda` files are offered for convenience, as they load directly into your R session with proper data types already set. Accessing the raw files can be useful for reproducibility, using the data in other tools, or for specific data manipulation needs not covered by the pre-processed `.rda` versions.

Please explore the `inst/extdata` directory (once files are organized in Step 5 of the data documentation improvement plan) to see the full list of available raw and external files.

Owner

Name: Serdar Balcı
Login: sbalci
Kind: user
Location: İstanbul, Turkey
Company: http://www.serdarbalci.com/

Website: https://sbalci.github.io/
Twitter: serdarbalci
Repositories: 11
Profile: https://github.com/sbalci

MD, Pathologist https://www.linkedin.com/in/serdar-balci-md-pathologist/

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}

GitHub Events

Total

Issues event: 67
Watch event: 4
Delete event: 12
Issue comment event: 30
Push event: 101
Pull request event: 10
Fork event: 2
Create event: 9

Last Year

Issues event: 67
Watch event: 4
Delete event: 12
Issue comment event: 30
Push event: 101
Pull request event: 10
Fork event: 2
Create event: 9

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 55
Total pull requests: 7
Average time to close issues: about 3 years
Average time to close pull requests: 1 minute
Total issue authors: 3
Total pull request authors: 1
Average comments per issue: 0.69
Average comments per pull request: 0.0
Merged pull requests: 3
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 11
Pull requests: 7
Average time to close issues: 1 day
Average time to close pull requests: 1 minute
Issue authors: 2
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 3
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

sbalci (52)
highwindmx (3)
ShinwookKang (1)

Pull Request Authors

sbalci (7)

Top Labels

Issue Labels

feature_request (6) enhancement (5) documentation (4) bug (2) question (1)

Pull Request Labels

codex (1)

ecosyste.ms

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