Methods for dynamic prediction

Program code
SE015-001

Program Duration
4 days

Dates
September 7th – 10th 2015

Credits
4

Tuition
1.300 € general partecipant
1.000 € PhD student
1.200 € SISMEC/SIB member
Fee includes: hotel accomodation, meals, bus transfer from Bergamo to Ponte di Legno and back, EXPO site entrance

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Methods for dynamic prediction

Multi-state models and landmarking

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Description

Prediction models play an important role in medicine to guide treatment decisions and to inform patients on their prognosis. The vast majority of prediction models developed in the medical literature have been designed to predict (disease-free) survival from diagnosis or start of treatment. But in clinical practice the patient regularly returns to the physician and it is important to be able to provide updated predicted probabilities of survival, taking into account clinical events that may have occurred, or clinical measurements that may have been made, between start of treatment and the time of prediction. Such prediction models, to be used after start of treatment and taking into account time-dependent information, are called dynamic prediction models.

In this course we focus on the development and validation of dynamic prediction models in clinical survival analysis. It will be discussed how dynamic prediction probabilities can be obtained using traditional models and new approaches will be presented that have been developed in the last few years. The course will consist of a mix of lectures and computer practicals.

Topics covered

  • The need of dynamic prediction and how to obtain it
  • Predictive accuracy of (dynamic) prediction models
  • Landmarking
  • Dynamic prediction using biomarkers and in multi-state models
  • Dynamic prediction using genomic data

Program Coordinator

Maria Grazia Valsecchi & Laura Antolini
Center of Biostatistics for Clinical Epidemiology, Department of Health Sciences, University of Milano-Bicocca

Location

Ponte Di Legno (Brescia)

Application Deadline

15 May 2015 / 2nd call for application is open! New deadline 30th June

Prerequisites

Intermediate knowledge of survival analysis, basic knowledge of the R package.

Target Audience

Applied statisticians, in particular biostatisticians are actively involved clinical research. Graduate PhD students interested in survival analysis.

For further information, Contact:

statisticalps@unimib.it

Methods for dynamic prediction Bicocca Summer School
Expo 2015 Università per EXPO 2015 Comune di Milano

In cooperation with

ESN ESN Bicocca

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