Summer Schools & Short Courses


Short Course "An Introduction to Multiplicity in clinical trials"

1st December 2022, from 2.30 – 5.30 pm

SpeakerGiacomo Mordenti – Head of Evidence Generation, Daiichi Sankyo Europe GmbH

TitleAn Introduction to Multiplicity in clinical trials

Abstract: Multiplicity of inferences is present in virtually all the experiments and it is particularly critical for clinical trials. The usual concern with multiplicity is that, if it is not properly handled, has the potential to severely undermine the evidence generated from such experiments. In particular claims for safety and effectiveness of a drug may be made as a consequence of an inflated rate of false positive conclusions. Control of the study-wise rate of false positive conclusions at an acceptable level α is an important principle and is often of great value in the assessment of the results of confirmatory clinical trials. The presentation will cover:

  • Introduction to the concept of multiplicity
  • How it affects statistical conclusions of an experiment
  • Introduction to the most common methods for handling multiplicity
  • Introduction to the graphical models for multiplicity
  • Case study
  • Conclusions

Short Course "Time Series Models"

14th-15th June 2022

SpeakerDante Amengual – CEMFI

Title: Time Series Models

Outline: This course studies statistical models for describing and predicting economic and financial time series, and analyzing the interrelations suggested by economic theory.


  1. Univariate time series: Stochastic processes. Serial correlation and stationarity. Prediction theory and Wold decomposition. ARMA models. Temporal aggregation.
  2. Multivariate time series: Correlation structure and stationarity. VARMA models. Contemporaneous aggregation, marginalization, and causality. Impulse-response analysis. Dynamic models with latent variables. Kalman Ölter.
  3. Integration and non-linear models: Integrated processes. Cointegration. Non-linear models. ARCH and stochastic volatility. Dynamic models with time-varying parameters. Hamilton Ölter.