Preparatory Sciences

Module Medical Statistics

The course aims to introduce the student to the elementary principles of medical research, where the object of study is not a single individual but a collective. Students will acquire the ability to also understand literature articles with concrete examples applied to clinical practice. The general objective is the knowledge of the main topics of medical statistics of interest for the degree course. Knowledge of the main models and theorems of medical statistics will be acquired and to apply them correctly to the qualitative and quantitative description of real cases by testing hypotheses. Furthermore, the student will acquire the ability to broaden and deepen the issues of medical statistics and its applications autonomously.

The teaching will take place through lectures.

If the teaching is given in a mixed or remote way, the necessary changes with respect to what was previously stated may be introduced, in order to respect the program envisaged and reported in the Syllabus.

The experimental design - Measurement and errors. The variability of biological, clinical and laboratory data. Statistical nature of the observations. Collection, classification, transformation and graphic representation of data.

Presentation of a case study; contingency tables - Frequency distributions; histograms; box and mustache diagrams. Position indices: the means (arithmetic, geometric, harmonic), mode, median, quartiles, percentiles. Variability indices: range of variation, deviance, variance, standard deviation, coefficient of variation.

Introduction to probability distributions. Application of probability in the biomedical field: Bayes' theorem. Diagnostic tests: Sensitivity, specificity and predictive values. Normal (or Gauss) distribution. The standard normal variable and its probability distribution.

General problems and sampling methods, sampling errors. Estimation of the parameters of a population: Confidence intervals of means.

Statistical significance test: null hypothesis, type I and II error, significance level, P value and power of a statistical test. Choice of statistical tests. Parametric and non-parametric tests for independent and dependent data. Z-test and t-test on a sample mean. Student's t-test for paired data and for unpaired data. Analysis of variance with one or two classification criteria (ANOVA for paired and unpaired data). Student-Newman-Keuls test for multiple comparisons. Nonparametric tests for unpaired data (rank sum test) and for paired data (Wilcoxon signed rank test). Kruskal-Wallis test. Friedman test. Chi-square test.

Which of the following could be considered as a ratio scale variable?

a) Occupation of the participants

b) Time taken to complete a physiotherapy program

c) Scores on a 5-point scale for service satisfaction

d) None of the above

Which of the following indices is not a central trend measure?

a) Medium

b) Coefficient of variation

d) Mode

d) Median