Learning objectives
- Bases of statistics and computer science
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.
- Integrity of Research and Good Clinical Practice
The primary objective of this educational module is to provide knowledge, cultivate virtues and develop the necessary skills to actively implement the ethical, professional and legal standards that underpin research integrity. This module values critical thinking and fosters an open dialogue on the quality of research.
Upon completion of the module each student will be able to:
1. Distinguish “normal” clinical practice from good clinical practice in research.
2. Understand and apply the principles and norms set forth in the new “Costituzione Etica” for Orthoptists and Assistants in Ophthalmology published in June 2021.
3. Be aware of the principles and norms set forth in the Belmont Report, Declaration of Helsinki, Guideline for Good Clinical Practice, European Code of Conduct for Research Integrity (ECoC).
4. Correctly apply principles and norms to research activities.
5. Recognise conflicts of interest (financial, personal, intellectual, etc.) and how they can affect our evaluations, judgement, decisions and behaviour.
6. Recognise cases of research misconduct and detrimental research practices: identify the violated principles/norms and decide the “best” course of action.
7. Recognise virtues (e.g. intellectual honesty, responsibility for one’s actions, etc.) and virtuous conduct in research.
8. Cultivate virtues in practice by seeking the “golden mean” (Aristotle) in each specific context.
9. Identify the different types of “goodness” (Von Wright, 1963) in research and in the European Code of Conduct for Research Integrity (ECoC).
-Information processing systems
To provide students with knowledge basic computer technologies available today.
Prerequisites
none
Course unit content
- Bases of statistics and computer science
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.
Elements of correlation and linear regression.
The course will conclude with an introduction to Machine Learning and related examples of application in the scientific field.
- Integrity of Research and Good Clinical Practice
It is essential to distinguish “normal” clinical practice from good clinical practice in research.
The Orthoptist/Assistant in Ophthalmology must be familiar with the principles and norms included in the new “Costituzione Etica”, which represents a professional ethical standard.
As regards research, in this module, two complementary approaches to Research Integrity will be developed:
1) The “principle-based” approach
2) The “virtue-based” approach
During the course we will perform a further exercise to stimulate reflection and discussion on Research Integrity. The starting point will be the question: “What is goodness?”.
The philosopher George Henrik von Wright thought that “goodness” could be divided into categories.
We will identify the different types of goodness in the context of research and in the content of the European Code of Conduct for Research Integrity (ECoC).
-Information processing systems
Short account of historical evolution from the first inventions in the 1600s to the present Introduction to computer science and the use of computers General concepts on the operating principles Functional analysis of the structure of a processor Hardware: CPU Memory I/O devices Binary system and Boolean operators Information (text, numbers, images, sounds) and its digital representation
Software
Basic software and operating systems
Application software
Notes on programs and algorithms
Full programme
- Bases of statistics and computer science
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, Jasp, Jamovi, and SPSS.
Introduction to Machine Learning: Classification and Regression; theoretical overview of the main supervised learning algorithms (logistic regression, linear regression, decision trees, and neural networks); examples of application in the scientific field.
- Integrity of Research and Good Clinical Practice
It is essential to distinguish “normal” clinical practice from good clinical practice in research.
The Orthoptist/Assistant in Ophthalmology must be familiar with the principles and norms included in the new “Costituzione Etica”, which represents a professional ethical standard.
As regards research, in this module, two complementary approaches to Research Integrity will be developed:
1) The “principle-based” approach is focused on principles, rules, duties, or responsibilities included in the main international ethical standards (Belmont Report, Declaration of Helsinki, Guideline for Good Clinical Practice).
By learning about the Tuskegee Syphilis Study (1932-1972), the student will understand the rationale underlying the Belmont Report, that defined the fundamental ethical principles and that led to the establishment of the Institutional Review Boards (Research Ethics Committees) in the United States.
The Good Clinical Practice principles will be examined and the informed consent process will be explored in detail, also taking into account that we must comply with the General Data Protection Regulation (GDPR) that requires patient consent to the processing of personal data.
Only in the eighties in the US, Research Integrity became a “hot topic” following a series of high-profile cases of data fabrication, falsification and plagiarism that led to the definition of “Research Misconduct” and of other “Questionable Research Practices”. During the course the “history” of Research Integrity will be presented to help the student better understand the current importance and pivotal role of this topic in all international research organisations and institutions that prompted the development of many codes of conduct such as the European Code of Conduct for Research Integrity (ECoC).
2) The “virtue-based” approach is focused on the devolopment of virtuous character traits. According to Aristotle people are only born with a disposition to acquire virtues. This disposition remains at a potential level and only develops by repeatedly practicing virtue over time. The primary task of education is character formation, that is accomplished by following the example of wise people, capable of acting in the “right” way according to the context. According to Aristotle a virtue is the midpoint (golden mean) between two vices (extremes), a lack of virtue or an excess of virtue.
Courage is the midpoint between too little courage (cowardice) and too much courage (recklessness).
During the course we will identify the virtues that should be practiced by all those involved in the research enterprise and we will discuss cases of lack of virtue and excess of virtue.
During the course we will perform a further exercise to stimulate reflection and discussion on Research Integrity. The starting point will be the question: “What is goodness?”.
The philosopher George Henrik von Wright thought that “goodness” could be divided into categories.
We will identify the different types of goodness in the context of research and in the content of the European Code of Conduct for Research Integrity (ECoC).
-Information processing systems
Short account of historical evolution from the first inventions in the 1600s
to the present Introduction to computer science and the use of computers
General concepts on the operating principles
Functional analysis of the structure of a processor
Hardware: CPU Memory I/O devices
Binary system and Boolean operators
Information (text, numbers, images, sounds) and its digital representation
Software
Basic software and operating systems
Bibliography
- Bases of statistics and computer science
M.M Triola, M.F. Triola : Fondamenti di Statistica, Ed. Pearson
W.W. Daniel : Biostatistica – Ed. Edises
A. Field. J. Miles, Z. Field : Discovering Statistics Using R, Ed. SAGE
- Integrity of Research and Good Clinical Practice
• Costituzione Etica. Federazione nazionale Ordini dei Tecnici sanitari di radiologia medica, delle professioni sanitarie tecniche, della riabilitazione e della prevenzione. Edizione Giugno 2021.
• ALLEA (All European Academies). Il codice di condotta europeo per l’integrità della Ricerca. Edizione aggiornata; Berlino 2018.
(https://www.allea.org/wp-content/uploads/2018/11/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017-Digital_IT.pdf)
• Buona Pratica nella Ricerca e nella Pubblicazione e Disseminazione dei Risultati. Linee Guida. Università degli Studi di Parma. 3 Agosto 2020.
(https://www.unipr.it/node/21810)
• The Virt2UE program. The Embassy of Good Science.
(https://www.embassy.science/wiki/Main_Page)
• Commissione per l’Etica della Ricerca e la Bioetica del CNR. Linee Guida per l’integrità nella ricerca. Revisione dell’11-4-2019.
(https://www.cnr.it/sites/default/files/public/media/doc_istituzionali/linee-guida-integrita-nella-ricerca-cnr-commissione_etica.pdf?v=1)
• David B. Resnik. Ethical virtues in scientific research. Account Res. 19(6):329-43, 2012. doi: 10.1080/08989621.2012.728908.
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521635/)
• Belmont Report; 1979.
(www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html)
• WMA Declaration of Helsinki; 2013.
(www.wma.net/en/30publications/10policies/b3/index.html)
• ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) Harmonised Guideline. Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice E6(R2); November 9, 2016.
(https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf)
• EQUATOR (Enhancing the Quality and Transparency of Health Research) Network.
(https://www.equator-network.org/)
• George Henrik von Wright. The Varieties of Goodness. 1963
(https://www.giffordlectures.org/lectures/varieties-goodness)
-Information processing systems
D. Sciuto, G. Buonanno, L. Mari, Introduzione ai sistemi informatici, McGraw-Hill
Lecture notes
Teaching methods
Lectures will be held on-site in compliance with safety standards. Supporting material will be available on the specific, student-reserved platform (Elly) and will include slide presentations, audio-video aids or video-recording of the lectures.
Assessment methods and criteria
- Bases of statistics and computer science
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.
In case of the persistence of the health emergency, the exams will be conducted remotely, as follows:
structured written test conducted remotely (by Teams and Elly). The candidate will remain connected with microphone and video camera turned on and will carry out the test under the control of the commission.
The test consists of multiple choice questions on the course contents (reference texts + documents uploaded to Elly during the course). There is no penalty for incorrect answers. Consultation of the didactic material will be allowed.
- Integrity of Research and Good Clinical Practice
In an oral examination the student must demonstrate that he/she has acquired the predefined learning outcomes of the course.
The student will be questioned on the content of the course to ascertain his/her knowledge and understanding.
The student’s ability to apply knowledge and understanding will be verified by evaluating his/her ability to apply principles, norms and strategies in good research practice and to concrete cases of research integrity violations.
Finally the student will demonstrate his/her ability to make judgements (make a decision) on the “best” course of action to follow in concrete cases of research integrity violations.
-Information processing systems
The achievement of the objectives of the module will be assessed through a written examination, consisting in closed and 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.
Students with disabilities, SLD, BSE must first contact Centro Accoglienza ed Inclusione (CAI) (https://cai.unipr.it/) for support.
Other information
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2030 agenda goals for sustainable development
2030 Agenda Sustainable Development Goals: codes 1; 2; 4
