STATISTICS APPLIED TO MEDICINE
- Overview
- Assessment methods
- Learning objectives
- Contents
- Bibliography
- Teaching methods
- Contacts/Info
None
In-class test, with multiple choices questions and exercises.
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).
a. The process of scientific knowledge and "evidence-based medicine": how to generate and verify a hypothesis.
b.Descriptive statistics: definition of variables, frequencly distribution.
c. Descrpitive 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. Popolation 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. Study design in medicine: observational studies vs. clinical trials.
Stanton A. Glantz
Statistica per discipline biomediche. Sesta Edizione.
McGraw-Hill
Lectures (30 hours) and practicals (6 hours).
Lesson notes available on the e-learning website.