Learning outcomes of the course unit
The module of Medical Statistics is designed to introduce the student to the basics of statistical thinking and its application in practice. The topics are geared to concrete problems of analysis and research and deal in particular with situations and cases drawn from the medical literature.
Starting from the multitude of information from which we are faced daily, the course aims to give students the statistical tools needed to describe and analyze the data, extract useful information and make informed decisions. Special emphasis will be put on statistical reasoning, interpretation and decision-making process. We will insist more on the conceptual understanding that the mechanical calculation, especially in light of the wide range of software available for analysis. The theory will be made explicit by means of practical exercises and teaching cases, therefore, the ultimate goal of the course is that the student learn "how to do" as well as knowing.
Course contents summary
The first part of the course will introduce the basics of statistical planning and experimental design.
Principles of probability and combinatorial analysis needed later in the course will be introduced, as well as the major probability distributions. This includes the binomial distribution, the Poisson distribution, the Normal and standard Normal distribution.
The second part of the course will address the methods of descriptive statistics. It will be shown how to recognize the type of data and how to summarize them in appropriate indicators.
The student will learn how to calculate measures of location (mean, median, mode), variability (variance, standard deviation), the coefficient of variation (CV), quantiles and their use.
In the final part of the course the general principles of statistical inference will be introduced.
The student will face the concepts of sampling distribution, type I and II error, power of a statistical test and operating curve.
The following methods will then be explained:
parametric tests - Student's t test, ANOVA 1 and 2 classification criteria.
non-parametric tests: - Wilcoxon test, Mann-Whitney, Kruskal-Wallis, Friedman test, median test, chi-square test, Fisher's exact test.
Linear regression and correlation.
Overview of multivariate statistics.
Introduction: medical statistics and related disciplines. Logic and statistical planning. Overview of combinatorial analysis: permutations, arrangements, combinations. Applications. Overview of probability calculations: simple and compound probability, Bayes theorem.
Odds. Odds ratios. Likelihood ratios. applications.
Probability distributions : binomial distribution, Poisson distribution, normal and standard normal distribution. Tables and their use.
Summarising data. Units of measure. Measurements of position, order and variation. Indices of central tendency, mean median, mode.
Indices of variability, variance, standard deviation, CV. Percentiles and their use.
General principles of statistical inference. Sampling distribution. Hypothesis and hypothesis testing. Type 1 and type 2 error. Power of a test and operating curve.
Parametric test : Student t-test, ANOVA with 1 and 2 classification criteria.
Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, median test, Chi-square test, Fisher exact test.
Overview of linear regression and correlation. Logistic regression.
1) Lecture notes
2) Stanton A. Glantz : Statistica per discipline Bio-mediche, ed. McGraw-Hill
3) Sidney Siegel, N. John Castellan Jr. : Statistica non parametrica, ed. McGraw-Hill
4) Internet resources and links
During classroom lectures, the topics contained in the program of the
module will be illustrated and commented.
At the end of each topic classroom exercises explaining the application of the theory in practice will follow. The formal procedure and the step by step execution of the necessary calculations will be described. Both manual solution and computer calculation will be shown.
The students will be particularly encouraged to use the open source statistical system "R" and the free software package Epi Info.
Assessment methods and criteria
The achievement of the objectives of the module will be assessed
through a written examination, mainly consisting in open questions on the
topics of the course. This will allow to ascertain the knowledge and the
understanding of both the theoretical bases and their consequences.
The written examination will include the resolution of problems, to assess the
achievement of the ability to apply the acquired knowledge to a
simulated biological or medical situation.
All parts of the written exam will be equally weighted in the final