STATISTICS APPLIED TO MEDICINE
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Bibliography
- 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).
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: population and sample (2 hours). Central Limit Theorem, distribution of the sample mean. Hypothesis test and confidence interval for a population mean. Hypothesis test for 2 or more population means. Hypothesis test for proportions (12 ore). Elements of statistics for randomized clinical trials (2 hours).
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.
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
Reception of students by appointment, please contact the teacher at: giovanni.veronesi@uninsubria.it