AHdeRojas/longitudinal_data_analysis

Scripts for the analysis of longitudinal data in R

Analysis of longitudinal data

Scripts for the analysis of longitudinal data in R

Material for the Course "Introduction to the analysis of longitudinal data with R"

Instructors: Filippo Biscarini, Andreia Amaral

This course will introduce students, researchers and professionals to the analysis of longitudinal data, i.e. data with a time component. The course will describe the main types of longitudinal data (e.g. treatments over timepoints, time series data), and a number of approaches to process and analyse such data.

Each day the course will start at 14:00 and end at 20:00 (CET). As a general rule, we'll have a longer break (30 minutes) at about 17:00 and two shorter breaks (10-15 minutes) later on during the day (to be decided flexibly depending on the sessions).

Day 1

• Lecture 0: General Introduction / Overview of the Course [Filippo]
  • General Introduction
• Lecture 1: Longitudinal data: examples and challenges [Filippo]
• Lab 1: First encounter with longitudinal data [Filippo]
  • 1.longitudinal_data.Rmd
• Lecture 2: The basic experimental setting: treatments and timepoints [Filippo]
• Lab 2: Treatments and timepoints in R [Filippo]
  • 2.1.treatments_and_timepoints.Rmd
  • 2.2.exercise.Rmd
  • 2.3.linear_regression_with_tidymodels.Rmd
• Lecture 3: Analysis of repeated records [Filippo]
• Lab 3: Models to analyse data with repeated records over time (multiple time points) and space (multiple locations) in R
  • 3.repeated_records.Rmd
• Lecture 4: Difference-in-differences (diff-in-diff) [Filippo]

Day 2

• Lab 4: diff-in-diff in R
  • 4.diff-in-diff.Rmd
• Lecture 5: Censored data and survival analysis
• Lab 5: Survival analysis in R
  • 5.survival_analysis.Rmd
• Lecture 6: Cross-validation: simple and with spatial, temporal (or other) data structure
• Lab 6: Cross-validation strategies in R
  • 6.1.cross_validation.Rmd
  • 6.2.block_cross_validation.Rmd
• Lecture 7: Time series and forecasting
• Lab 7: Time series and forecasting in R
  • 7.1.time_series_and_autocorrelations.Rmd
  • 7.2.forecasting.Rmd
  • 7.3.time_series_with_tidymodels.Rmd

Day 3

• Lecture 8: Introduction to Linear Mixed Models

  • Lecture 8
  • R code for Day 3

• Lab 8: Linear Mixed Models

  • Lab_8_slides

• Lab 9: Testing for the effects of variables in R

  • Lab_9_slides

• Lab 10: Group effect and Interaction between time and group in R

  • Lab_10_slides

• Lab 11: Parametric curves and prediction of random effects in R

  • Lab_11_slides

• Lab 12: Model diagnostics

  • Lecture 9

• Lab 13: Generalized Estimation Equations (GEE)

  • Lab_13_slides

• Lab 14: Generalized Linear Mixed Models (GLMM)

  • Lecture GLMM

  Day 4

• Lab 12: Continuation Model diagnosis

  • Model diagnostics

• Lab 15: GLM and GLMM in the framework of gene expression and analysis of time series experiments

  • Analysis of time effect, GLM and GLMM

• Lab 16: Endemic-epidemic modelling for infectious disease counts

  • hhh4