STATISTICS APPLIED TO MEDICINE
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
- Contacts/Info
None.
In-class test, with multiple choices questions and exercises. The exam will be organized so as to verify both the knowledge and the learning ability of the students (60%), and their ability to apply the knowledge in practical exercises (40%).
The main aim of the course is to illustrate the basic elements of statistics needed to critically read and correctly interpret the results of a quantitative medical research.
Descriptive and inferential statistics are framed into the scientific knowledge process and the concept of "evidence-based medicine". Practicals will focus both on simple exercises and on the reading and understanding of the “results” section of scientific paper(s).
Lectures: Scientific knowledge, inference and "evidence-based medicine" (2 hours). Descriptive statistics: frequency distribution, indices of location, symmetry and variability (6 hours). Probability: definition, proprieties, and application. Bayes’s theorem and diagnostic test accuracy. (4 hours). Binomial and normal distributions (2 hours). Inference: Central Limit Theorem, distribution of the sample mean. Hypothesis test and confidence interval for the mean of one or more populations. Hypothesis test for proportions (10 ore).
Practicals: Descriptive statistics (2 hours). Probability (2 hours). Reading of a paper from the scientific literature in the medical field (2 hours).
a. The process of scientific knowledge and "evidence-based medicine": how to generate and verify a hypothesis.
b. Descriptive statistics: definition of variables, frequency distribution.
c. Descriptive statistics: mode, median, mean
d. Variability and its measures: range, variance, standard deviation, variability coefficient
e. Symmetry
f. Probability: definitions and laws. Bayes's theorem and its application to diagnostic tests: sensitivity, specificity, predictive value, area under the ROC curve.
g. probability distributions: binomial and normal
h. Population and sample. Sampling methods. Distribution of the sampling mean and central limit theorem
i. Statistical inference: hypothesis test and confidence intervals
j. Inference on the mean: test Z, test t for independent samples, test t for matched samples, ANOVA
k. Inference on proportion(s): test Z, chi-square test, Fisher's exact test
l. Elements of statistical analyses for randomized clinical trials.
Lectures (24 hours), corresponding to 8 hours per CFU. The remaining 12 hours (4 per CFU) are dedicated to practicals (6 hours), and to home assignments and readings from the scientific literature (6 hours). Lesson notes available on the e-learning website
Reception of students by appointment, please contact the teacher at: giovanni.veronesi@uninsubria.it
Professors
Borrowers
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Degree course in: SCHOOL OF DENTISTRY