StatisticAlps 2020, 9th Edition: The analysis of longitudinal and life history data

Program code
STATISTICALPS

Program Duration
4 Days

Dates
September 6-11, 2020.

Credits
2 ECTS credits

Tuition

General participant: 1400 €
PhD student: 1100 €
SIB/SISMEC/ISCB member: 1300 €

Inclusive of teaching material, bus transfer, hotel accommodation and meals (from dinner of the 1st of March to the breakfast of the 6th March).

StatisticAlps 2020, 9th Edition: The analysis of longitudinal and life history data

New Dates

We deeply regret to inform that the University of Milan-bicocca is obliged to rschedule the Winter School. have been released: September 6-11, 2020. We are sorry for the inconveniences this situation may have created, we are available to provide support and information to all of you.

Description

Statistic Alps is a residential training course on advanced statistical issues of interest in clinical research. It is traditionally held in Ponte di Legno, a place in the Alps with beautiful surroundings. The course has reached the 9th edition and this year will be in a winter format.

The general aim of 2020 edition is to provide an introduction to statistical methods for the analysis of longitudinal and life history data. An emphasis will be given to the kinds of data arising in epidemiology and public health research, with some issues being specific to the analysis of data from clinical studies. We will begin with a focus on common approaches for the analysis of repeated measurements from individuals over common scheduled assessment times, including mixed effects models, generalized estimating equations, and autoregressive models. Models and methods will then be discussed for the analysis of life history data obtained from continuous observation of individuals who are subject to right-censoring. The assumptions justifying the various approaches to analysis will be highlighted, and the interpretation of covariate effects and other possible estimands will be emphasized. Recurring themes will include robustness, the implications of a dependence between the longitudinal or life history process and the observation process (i.e. missing data, censoring and informative observation mechanisms), and causal inference.

Substantive examples from medical science will be used throughout the course to motivate the methods and illustrate the different interpretations given to estimates of intervention and other covariate effects.

Contents covered