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Showing content from https://sbalci.github.io/ClinicoPathJamoviModule/ below:

Comprehensive Analysis for Clinicopathological Research • ClinicoPath

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).
• 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.

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

(Comprehensive swimmer plots for visualizing patient timelines)

(Statistical distribution generator and analyzer)

(Tools for data summary with summarytools integration)

🔬🔬🔬🔬 UNDER CONSTRUCTION 🛠⛔️⚠️🔩

(sjPlot Integration for Social Science Research)

(Line Chart for Time Series and Trend Analysis)

(Creates treemap visualizations for categorical data)

(Create interactive StreamGraphs using R streamgraph package.)

(Fit-for-Purpose Clinical Visualizations)

🔬🔬🔬🔬 UNDER CONSTRUCTION 🛠⛔️⚠️🔩

🔬🔬🔬🔬 UNDER CONSTRUCTION 🛠⛔️⚠️🔩

🔬🔬🔬🔬 UNDER CONSTRUCTION 🛠⛔️⚠️🔩

🔬🔬🔬🔬 UNDER CONSTRUCTION 🛠⛔️⚠️🔩

You can install the development version from GitHub with:

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

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.

Buy me a coffee

https://paypal.me/serdarbalci

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.

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