10596373 - Statistics for health economics 2021/2022

Google Classroom

Students should join the course using Google Classroom (code: 5vxwwfo). Materials and announcements about the course will be posted exclusively on Google Classroom.

Schedule (Orario lezioni)

  • Monday 9-11 (room 2C)
  • Tuesday 9-11 (room 2C)
  • Wednesday 9-11 (room Vittorio Marrama)

Language (Lingua)


Final grade (Modalità di valutazione)

The final grade will be based on a written exam (which will weigh 50% of the final grade) and a practical application (which will weigh 50% of the final grade). The former will require solving theoretical problems. The latter will include a data analysis exercise using R.

The calendar of the exam sessions is available on the course webpage from the courses catalogue.

Teaching delivery modality (Modalità di svolgimento)

Classes will be in-person and will take place as scheduled. Lectures will be streamed online according to schedule for students unable to attend in person due to reached limit of classroom occupancy or public health-related reasons or because resident abroad and subject to travel restrictions. The link to attend remote classes is available on the School webpage.

Prerequisites (Prerequisiti)

Fundamental prerequites: calculus (limits, integrals, derivatives, matrices/vectors) and introductory statistics (mean, variance, chi-squared test, linear correlation, linear regression). Knowledge of probability theory is not required but recommended.

Topics (Programma)

The content of the course will consist of the following topics, where the numbering reflects their temporal sequence. The number of hours is approximate and it includes computer lab.

1.    Overview of the course (2 hours)
2.    Probability: basic definitions, independence, conditional and Bayes theorem, distributions (discrete, continuous, joint, marginal and conditional), moments (expected value, variance, covariance/correlation) and quantiles (6 hours)
3.    Likelihood (definition, maximization); maximum likelihood estimator (properties) (6 hours)
4.    Confidence intervals and test of hypotheses (6 hours)
5.    Study designs and measures of risk (2 hours)
6.    Contingency tables: analysis with 2 exposure levels, analysis with more than 2 exposure levels, analysis with confounders (12 hours)
7.    Generalized linear models: introduction, maximum likelihood estimation (2 hours)
8.    Generalized linear models: normal regression (model specification, estimation, interpretation, diagnostics) (10 hours)
9.    Generalized linear models: logistic regression  (model specification, estimation, interpretation, diagnostics) (10 hours)
10.    Predictive modelling (ROC curve, AUC) (6 hours)
11.    Matched data: McNemar test, conditional logistic regression, measures of interobservers agreement (Cohen's kappa) (4 hours)
12.    Generalized linear models: Poisson regression (model specification, estimation, interpretation, diagnostics) (8 hours)

Bibliography (Bibliografia di riferimento)

  • Course slides
  • Mood, Graybill, Boes. Introduction to the theory of statistics. McGraw-Hill
  • Piccolo. Statistica. Il Mulino
  • Fahrmeir, Kneib, Lang, and Marx. Regression: Models, Methods, and Applications. Springer
  • Agresti A. Categorical Data Analysis. Wiley
  • Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. Wiley
  • Crawley MJ. The R book. Wiley
  • Venables WN, Smith DM, and the R Core Team. An introduction to R. Available at: https://cran.r-project.org/manuals.html

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