95727 - LONGITUDINAL AND TIME-TO-EVENT DATA ANALYSIS

Course Unit Page

  • Teacher Jeanne Jacobine Duistermaat

  • Credits 6

  • SSD SECS-S/05

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)

Academic Year 2021/2022

Learning outcomes

By the end of the course the student knows the classical statistical models for dependent responses based on random components and the principal methods for time to event data analysis. The student acquires the skills required to solve real-world research problems.

Course contents

This course will introduce analysis tools and models for data collected in follow-up studies. These include longitudinally measured data such as blood levels (glucose, cholesterol, lead) and health scores, and time to event data such as death or disease recurrence in cancer patients.

The first weeks will be focussed on models for longitudinal data. We will cover topics such as models for the mean, models for the covariance, mixed effects models, model diagnostics and missing data.

Then we will proceed with proportional hazard models for survival data. After a brief rehearsal of the basics of survival analysis we will consider time dependent covariates.

We will finish the course with joint modelling of time to event and longitudinal data. When the focus is on the longitudinal outcome joint models might provide proper parameter estimates when the data are non-missing at random by modelling the missingness mechanism. When the focus is on the event data, joint models provide proper estimates when the time dependent covariate is endogenous and measured with an error.

We will analyse different datasets using R.

Readings/Bibliography

Applied longitudinal analysis. Second Edition. GM Fitzmaurice, NM Laird, JH Ware. Wiley series in probability and statistics. Chapter 1 to 10, 17 and 18.

Joint models for longitudinal and time-to-event data, with applications in R. D Rizopoulos. Chapman & Hall/CRC Biostatistics Series. Chapter 1, 3, 4 and 7.

Teaching methods

Lectures and class exercises including R.

Assessment methods

Written exam.

Teaching tools

Materials will be made available on Virtuale

Office hours

See the website of Jeanne Jacobine Duistermaat