10596373 - Statistics for health economics 2023/2024

Google Classroom

Students should join the course using Google Classroom (code: l6mqcg3). Materials and announcements about the course will be posted exclusively on Google Classroom.

Schedule (Orario lezioni)

  • Tuesday 12-14 (room Vittorio Marrama - 6th floor)
  • Wednesday 12-14 (room Vittorio Marrama - 6th floor)
  • Thursday 10-12 (room Vittorio Marrama - 6th floor)

Language (Lingua)


Final grade (Modalità di valutazione)

The final grade will be based on a written exam to assess methodological knowledge (50% of the final grade) and practical skills (50% of the final grade). The former will focus on theoretical/methodological problems. The latter will require elaborating and interpreting the output from an R data analysis.

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.

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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