Medical Decision Analysis, Diagnostic Evaluation, and Reliability Assessment for jamovi and R
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meddecide is a comprehensive R package and jamovi module bridging statistical methodology and practical clinical decision-making. As the diagnostic analytics and decision science engine of the ClinicoPath ecosystem, it equips clinicians, pathologists, and biomedical researchers with advanced tools for diagnostic test accuracy evaluation, Decision Curve Analysis (DCA), ROC curve modeling, inter-rater reliability assessment, regularized prediction modeling (LASSO Logistic), and precision/power sample size planning—all accessible through an intuitive graphical interface and reproducible R code.
In jamovi, twelve analyses are under the meddecide menu and the three kappa planners under Power → Power Analysis by meddecide. Menu titles are given as they appear in jamovi.
| Menu (subgroup) | Analysis | R function | What it does |
|---|---|---|---|
| meddecide (Decision) | Medical Decision | decision |
One test against a reference standard: sensitivity, specificity, predictive values (observed or at a prevalence you supply), likelihood ratios, diagnostic odds ratio and Youden's index with 95% confidence intervals; a Fagan nomogram. A zero cell is handled by an explicit rule (a ratio with an empty numerator is exactly 0). |
| meddecide (Decision Calculators) | Medical Decision Calculator | decisioncalculator |
The same measures from four 2 × 2 counts, with post-test probabilities and a Fagan nomogram. |
| meddecide (Decision Calculators) | Co-Testing Analysis | cotest |
Two tests used together (for example HPV and cytology): joint sensitivity and specificity, under conditional independence or with a dependence you specify, and post-test probabilities. |
| meddecide (Decision Calculators) | Sequential Testing Analysis | sequentialtests |
Two tests in sequence (serial, confirming positives or negatives) or in parallel: combined accuracy, predictive values and the number of people tested at each stage. |
| meddecide (Decision) | Combine Medical Decision Tests | decisioncombine |
All result patterns and the AND/OR (serial/parallel) rules for two or three tests against a reference standard. |
| meddecide (Decision) | Compare Medical Decision Tests | decisioncompare |
Two or three tests on the same patients: paired McNemar tests, paired differences in sensitivity and specificity (Newcombe), Cochran's Q. |
| meddecide (Decision) | Analysis Without Gold Standard | nogoldstandard |
Latent class model, penalized EM or composite-reference rules when no test is a perfect reference; checks conditional dependence. |
| meddecide (Decision Curve Analysis) | Decision Curve Analysis | decisioncurve |
Net benefit of risk models and clinical rules across threshold probabilities against treat all and treat none, interventions avoided, bootstrap intervals (pointwise or simultaneous). |
| meddecide (ROC) | Clinical ROC Analysis | enhancedROC |
ROC curves, AUC with DeLong intervals, cut-points (Youden's index, closest to top left, clinical presets), calibration and Brier score, frozen-model validation. |
| meddecide (ROC) | Advanced ROC Analysis | psychopdaROC |
Cut-point methods from cutpointr, threshold tables, confusion matrices, partial AUC, DeLong comparison of markers, NRI and IDI, bootstrap intervals. |
| meddecide (Agreement) | Interrater Reliability | agreement |
Cohen's, weighted and Fleiss' kappa, Krippendorff's alpha, Gwet's AC1/AC2, ICC, Lin's CCC, Bland–Altman, specific (positive/negative) agreement, rater clustering and more. |
| meddecide (Prediction Models) | LASSO Logistic | lassologistic |
Penalized logistic regression (LASSO, ridge or elastic net) with cross-validated λ and a data-suitability check. |
| Power (Power Analysis by meddecide) | Confidence Interval Approach for the Number of Subjects Required | kappaSizeCI |
Subjects needed so that the expected kappa confidence interval stays within the lower and upper limits you set. |
| Power (Power Analysis by meddecide) | Power Approach for the Number of Subjects Required | kappaSizePower |
Subjects needed to show that kappa exceeds a null value with the chosen power. |
| Power (Power Analysis by meddecide) | Lowest Expected Kappa for a Fixed Sample Size | kappaSizeFixedN |
The lowest kappa you can expect to demonstrate with a given number of subjects. |
The calculations are checked against independent R packages before each release (see NEWS.md). That shows the arithmetic is right; it does not establish that a test or model is clinically valid for your setting.
Open Three Imperfect Tests Without a Gold Standard in the jamovi Data Library.
The file nogoldstandard_validation.omv contains
190 simulated patient records and a configured nogoldstandard analysis of New_Test,
Reference1 and Reference2, with Positive selected for each. Neither reference test
is assumed perfect. It includes 1,000 bootstrap refits at 95% confidence, seed 42,
analysis diagnostics and a plain-language summary. This replaces the pathologist-reading
example; reader reproducibility belongs in the agreement examples.
The high-positive latent class is not automatically disease, and three tests cannot establish conditional independence. The filename describes a simulated validation-study scenario, not demonstrated clinical validation. See the example guide for settings and interpretation.
- Open jamovi (2.7 or later; meddecide 1.1.3 needs at least 2.7.27).
- Click the Modules (+) button in the top right corner, then jamovi library.
- Search for meddecide and click Install.
To install a build that is not yet in the library, download meddecide_<version>.jmo from the
GitHub releases, then choose
Modules → Sideload in jamovi and select the file.
# Install development version from GitHub
remotes::install_github("sbalci/meddecide")Each R function takes the same options as the jamovi panel. Level arguments (the positive and
negative levels) are required; pass NULL for one you do not use.
library(meddecide)
# 1. One diagnostic test against a reference standard
data("histopathology", package = "meddecide")
histopathology$Reference <- factor(histopathology$`Golden Standart`, levels = c(1, 0),
labels = c("Disease", "No disease"))
histopathology$Test <- factor(histopathology$`New Test`, levels = c(1, 0),
labels = c("Positive", "Negative"))
decision_res <- decision(
data = histopathology,
gold = "Reference", goldPositive = "Disease", goldNegative = "No disease",
newtest = "Test", testPositive = "Positive", testNegative = "Negative"
)
decision_res$ratioTable
# 2. Decision curve analysis of three risk models
data("dca_test_data", package = "meddecide")
dca_test_data$cardiac_event <- factor(dca_test_data$cardiac_event)
dca_res <- decisioncurve(
data = dca_test_data,
outcome = "cardiac_event", outcomePositive = "Yes",
models = c("basic_model", "enhanced_model", "biomarker_model"),
decisionRulePositive = NULL
)
# 3. Agreement between two raters (Cohen's kappa)
histopathology$`Rater 1` <- factor(histopathology$`Rater 1`)
histopathology$`Rater 2` <- factor(histopathology$`Rater 2`)
agreement_res <- agreement(data = histopathology, vars = c("Rater 1", "Rater 2"))
agreement_res$irrtable
# 4. Subjects needed for a kappa of 0.80 with a 95% CI between 0.70 and 0.90
kappaSizeCI(outcome = "2", kappa0 = 0.80, kappaL = 0.70, kappaU = 0.90,
props = "0.5, 0.5", raters = "2", alpha = 0.05)- Module Website & Vignettes: https://www.serdarbalci.com/meddecide/
- ClinicoPath Umbrella Ecosystem: https://www.serdarbalci.com/ClinicoPathJamoviModule/
- GitHub Repository: https://github.com/sbalci/meddecide/
- Issue Tracking & Feedback: GitHub Issues
If you use meddecide in your research or publications, please cite:
@manual{balci2026clinicopath,
title = {ClinicoPath: jamovi Module for Clinicopathological Research},
author = {Serdar Balci},
year = {2026},
url = {https://www.serdarbalci.com/ClinicoPathJamoviModule/},
doi = {10.5281/zenodo.3997188}
}GPL (>= 2) — see the LICENSE.md file for details.