User guide

This page presents the user guide of Leaspy. Leaspy is a software package for the statistical analysis of longitudinal data, particularly medical data that comes in a form of repeated observations of patients at different time-points.

• 1. Get started
  • 1.1. Installation
  • 1.2. Leaspy in a nutshell
• 2. Mathematical Background
  • 2.1. Mixed effect models
  • 2.2. Riemanian framework
    • 2.2.1. Disease progression represented as trajectory
    • 2.2.2. Trajectory shape defined by Riemmanian metric
    • 2.2.3. Individual variability define as initial conditions
  • 2.3. References
• 3. Models
  • 3.1. Logistic Model
    • 3.1.1. Data
    • 3.1.2. Mathematical background
    • 3.1.3. References
  • 3.2. Joint Model
  • 3.3. Mixture Model
  • 3.4. Covariate Model
• 4. Algorithms
  • 4.1. Fit
    • 4.1.1. Prerequisites
    • 4.1.2. Running Task
    • 4.1.3. Output
    • 4.1.4. Setting Options (Different Models)
  • 4.2. Personalize
  • 4.3. Estimate
  • 4.4. Simulate
  • 4.5. Data Generalities
  • 4.6. References
• 5. Models Selection and Evaluation
  • 5.1. Convergence diagnosis
  • 5.2. Likelihood based metrics
  • 5.3. Reconstruction metrics
  • 5.4. Prediction metrics
• 6. Link to Jupyter Notebook Tutorial
• 7. To Go Further
  • 7.1. Model Implementation
    • 7.1.1. DAG Intuition and Structure for Models
  • 7.2. What kind of scientific question could be answered with leaspy?
    • 7.2.1. Used in different context
    • 7.2.2. Used for different tasks
  • 7.3. References