DynNom — User Guide

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1. Page Structure

This app contains 5 tabs. Use the left panel for settings and the right panel for results:

• Data Preview — View the loaded data table (covariates and outcome)
• Nomogram — Display the logistic regression model nomogram
• Model Summary — Model summary with coefficients, OR, and P-values
• Prediction — Enter patient variable values to predict class probabilities
• Help — This page

2. Workflow

1. Select a data source (Demo data or upload CSV/XLSX) on the left and click 'Load Data'
2. Select the outcome variable and covariates (at least 2)
3. Click 'Run Analysis' to build the logistic regression model
4. Switch to the 'Prediction' tab and enter patient variable values
5. Click 'Predict' to view class probabilities and nomogram annotation

3. Prediction Details

In the Prediction tab:

• Enter values for each covariate (numeric or categorical)
• Results include: variable points, total points, linear predictor, and predicted probability for each class
• The nomogram on the right uses red arrows to mark the current patient's position

4. Data Format Requirements

• Outcome variable: Binary (0/1) or categorical, representing the class label
• Covariates: Numeric or categorical, at least 2 required
• Missing values will be automatically removed

5. Model Principle

This app is based on logistic regression:

P(Y=1|X) = 1 / (1 + exp(-(β₀ + β₁X₁ + β₂X₂ + ... + βₖXₖ)))

Where exp(β) is the odds ratio (OR).

The nomogram maps the logistic model's linear predictor to Points, then reads predicted probabilities from the Total Points.

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For questions, contact WeChat: icecoler