# MEDICAL STATISTICS

## 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.

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.

## Prerequisites

none

## 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.

Overview of special charts (mosaic plot, box-percentile plot, parallelviolin

plot, etc).

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.

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.

## Course contents

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.

Power analysis and sample size determination.

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.

Linear regression and correlation. Multiple regression. Logistic regression.

Computer exercises with the software "R" and Epi Info.

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.

## Recommended readings

STANTON A. GLANTZ: Statistica per discipline Bio-mediche, McGraw Hill

Michael J. Crawley "The R book" , Ed. Wiley

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

## Teaching methods

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.

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

evaluation.

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

evaluation.