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: Tools for Kaplan-Meier estimation, log-rank tests, and Cox proportional hazards models for single, comparison, and multiple variable analyses.
• Diagnostic Accuracy: Evaluation of diagnostic tests using Receiver Operating Characteristic (ROC) curve analysis, decision curve analysis (DCA), and screening test calculators.
• Agreement and Reliability: Assessment of inter-rater reliability using statistics such as Cohen’s Kappa and the Intraclass Correlation Coefficient (ICC).
• Laboratory Quality Control: ISO 15189 compliant statistical process control with Shewhart, CUSUM, and EWMA control charts, Six Sigma metrics, method validation protocols, reference interval establishment, and measurement uncertainty estimation.
• Spatial Statistics & Digital Pathology: Comprehensive spatial analysis tools including Ripley’s K-function, spatial autocorrelation (Moran’s I, Geary’s C), LISA analysis, and Haralick texture analysis for digital pathology applications.
• Data Visualization: A collection of advanced plotting tools, including waterfall plots, swimmer plots, raincloud plots, and advanced bar plots for intuitive data exploration and presentation.
• Data Preprocessing: Utilities for data quality checking, management of missing data, 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.

For a comprehensive guide to all the documentation available for this package, please see the Documentation Hub.

A jamovi 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.

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

DOI 10.17605/OSF.IO/9SZUD

https://osf.io/9szud/

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

Download and install jamovi.

Download the relevant jmo file for your operating system from

• releases

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/

remotes::install_github("sbalci/ClinicoPathDescriptives")

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/

remotes::install_github("sbalci/jjstatsplot")

https://github.com/sbalci/jsurvival

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

remotes::install_github("sbalci/jsurvival")

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

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

remotes::install_github("sbalci/meddecide")

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

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

(Similar to Age Pyramid with more styling options)

(Similar to Alluvial Diagrams with more styling options)

(Comprehensive missing data analysis and multiple imputation)

(Advanced outlier detection with multiple statistical methods)

(Enhanced categorical summary)

(Comprehensive distribution analysis)

(Enhanced summary statistics for continuous and date variables)

(Comprehensive swimmer plots for visualizing patient timelines)

(Enhanced Table One with pivottabler)

(Statistical distribution generator and analyzer)

(Tools for data summary with summarytools integration)

(Treatment toxicity profile analysis)

(Venn and Upset diagrams)

(Treatment response analysis)

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(Advanced bar charts - 5 ways)

(Advanced raincloud plot with longitudinal support)

(Predictive Power Score Analysis)

(Fast scatter plots for large datasets)

(sjPlot Integration for Social Science Research)

(Advanced Violin Plots for Data Distribution)

(Create Waffle Charts to visualize distributions)

(Line Chart for Time Series and Trend Analysis)

(Lasso-Cox Regression for Variable Selection in Survival Analysis)

(Multivariate Exploration)

(Automatic Plot Selection Based on Variable Types)

(TidyDensity - Distribution Analysis & Simulation)

(Violin Plots to Compare Within Groups)

(Creates treemap visualizations for categorical data)

(Create interactive StreamGraphs using R streamgraph package.)

(Scatter Plot for Continuous Variables)

(Treatment Pathway Alluvial Plot)

(Kaplan-Meier Survival Analysis for Single Group)

(Survival for whole group, no explanatory factor)

(Cut-off & Univariate Survival Analysis)

(Power Analysis for Survival Studies)

(Predictive Performance Over Time)

(Advanced time interval analysis with quality assessment)

(Fit-for-Purpose Clinical Visualizations)

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You can install the development version from GitHub with:

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

R: 4.0.5

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

Made possible via the codes, help, and guidence of

• jamovi developer Jonathon Love,
• finalfit developer Ewen Harrison,
• ggstatsplot developer Indrajeet Patil,
• tangram developer Shawn Garbett,
• easystats developers ,
• report ,
• tableone,
• survival,
• survminer,
• vtree developer Nick Barrowman,
• easyalluvial, developer Björn Oettinghaus, and
• rstats community.

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

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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:

# 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.")
}

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.

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

• Example: heartdisease.xlsx

  # 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).

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/.

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

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.