
Biostatistics
Code: 102947Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Medicine | FB | 1 |
Contact lecturer
- Name :
- José Rios Guillermo
- Email :
- jose.rios@uab.cat
Teaching staff
- Jesus Giraldo Arjonilla
- Ferran Torres Benitez
- Gianluigi Caltabiano
- José Rios Guillermo
- Albert Navarro Gine
Group languages
You can consult this information at the end of the document.
Prerequisites
There are no official prerequisites, but it is recommended that the student have some prior knowledge of mathematics, including probability concepts, statistical estimation (percentages, means, etc.), and graphical representations (bar charts, pie charts, etc.).
Objectives
The subject of Biostatistics is attended during the first year of the Degree in Medicine and is part of the basic education subjects. Its main objective is to introduce students to the knowledge and use of basic knowledge tools in accordance with the scientific method.
The subject will address the problems related to the investigation in the field of Medicine with the statistical method and the theory of probabilities. This approach will allow to quantify, in a precise way, significant relationships between the different phenomena -biological, psychological and social-related to human health and pathology from the perspective of Medical Research.
To achieve these goals, the student will have to work with various conceptual, methodological and instrumental tools necessary to develop a vision of Medicine in accordance with scientific rigor.
The Biostatistics course is related to other required courses such as Epidemiology or Preventive Medicine and Public Health, as well as to elective courses such as Clinical Pharmacology and the Bachelor's Thesis (TFG).
Learning outcomes
- Recognise the principles of the scientific method for obtaining laws of general validity.
- Demonstrate, in professional activity, a perspective that is critical, creative and research-oriented.
- Formulate hypotheses and compile and critically assess information for problem-solving, using the scientific method.
- Demonstrate basic research skills.
- Use information and communication technologies in professional practice.
- Differentiate between the various types of variables and ways of processing these.
- Organise biomedical data for subsequent processing and analysis by computer.
- Estimate population parameters based on those of the corresponding samples.
- Formulate and compare hypotheses and identify associated errors.
- Determine the sample size needed to compare hypotheses.
- Identify the statistical technique needed to compare hypotheses and choose a procedure from a statistical package to execute this technique.
- Interpret statistical results appropriately.
- Differentiate between the concepts of sample and population.
- Explain the role of probability theory in statistical inference.
- Recognise the need for representative samples, and the importance of the sampling methods.
- Explain the application of probability in the mechanisms that govern decision theory and its applications to automatic diagnosis.
- Calculate sensitivity, specificity and predictive values as measures for evaluating diagnostic tests.
- Interpret statistical data in medical literature.
- Critique scientific papers on biostatistics.
- Construct hypotheses and test them, assessing the validity of the data compiled.
Contents
A. Univariate descriptive statistics
B. Bivariate descriptive statistics
C. Probability Theory.
D. Bayes' Theorem applied to the evaluation of diagnostic tests
E. Random variables and theoretical statistical distributions
F. Central Limit Theorem and Moivre's Theorem
G. Statistical estimation of parameters
H. Statistical hypothesis testing. Comparison of means
I. ANOVA and Linear Regression
J. Hypothesis testing for categorical variables
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| SELF-STUDY | 40 | 1.6 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 |
| CLASSROOM PRACTICES | 8 | 0.32 | 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20 |
| Narrative records/written works | 26.5 | 1.06 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 |
| READING ARTICLES/REPORTS OF INTEREST | 5 | 0.2 | 1, 2, 3, 4, 5, 6, 12, 13, 14, 15, 16, 18, 19 |
| THEORY (TE) | 27 | 1.08 | 1, 3, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 |
| ORAL EXPOSITION OF WRITTEN WORKS | 15 | 0.6 | 1, 2, 3, 4, 5, 11, 12, 14, 16, 18, 19, 20 |
| LABORATORY PRACTICES (PLAB) | 17.5 | 0.7 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 |
Theory: Theoretical classes will be taught with face-to-face methodology - master classes - although the interaction and participation of the students will be made possible and stimulated to the maximum. The classes will be supported by audiovisual media. The material used in class by the teacher will be available on the Virtual Campus of the subject; students are recommended to print it and take it to class, to use it as a support when it comes to taking notes. The student will be encouraged to deepen into the knowledge acquired in class using the recommended bibliography and simulation software.
specialized seminars: Given the character and orientation of the subject, classes of problems will play a key role in its development and in the learning of the subject. Based on specific practical problems or reading the results of a scientific article, students will be able to apply the knowledge acquired in theory classes and personal study.
Specialized seminars will introduce the dynamic methodology and selected sets of practical cases that the student will have to solve through the statistical software of reference, in order to achieve the objectives pursued by the subject.
Classes of laboratory practices: Practical classes are a fundamental point for the correct fulfillment of the objectives of the subject. During them the student will have to solve practical cases, previously selected and discussed, through statistical software. The practices will be carried out individually or in small groups in computer rooms.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Practice: Assessments written through objective tests: Multiple choice test | 20% | 3 | 0.12 | 1, 2, 3, 4, 6, 7, 8, 11, 12, 15, 16, 20 |
| Theory: Assessments written through objective tests: Multiple choice test | 60% | 4 | 0.16 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20 |
| Elaboration of practical works | 10% | 3 | 0.12 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20 |
| Workshop Problem's solving | 10% | 1 | 0.04 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 |
This subject contemplates two types of assessment: a general, valid for all students and second one, only valid for students with a second or later enrollment.
Specifically:
MODALITY 1: available for all students.
The competences of the subject will be assessed with multiple choice exams (Theory: Tests T1 and T2, 60% of the mark; Practices: P1 and P2 tests, 20% of the mark), live resolution of problems during some Workshops (S, 10%) and practical work (TP, 10% of the note), according to the following scheme:
| THEORY | % |
|
|
|
| 1st partial test | 25 |
| 2nd partial test | 35 |
| PRACTICES |
|
| 1st partial test | 10 |
| 2nd partial test | 10 |
| WORKSHOPS-Theory |
|
| Solving Statistical Problems | 10 |
| Practical Works | 10 |
Attendance at practical sessions as well as at workshops is mandatory. The minimum global qualification required to pass the subject is 5 points.
MODALITY 2: only available to students with a second or later enrollment.
The competences of the subject will be evaluated with multiple choice exams with (THEORY: T1 and T2 tests), according to the following scheme:
| THEORY | % |
|
|
|
| 1st partial test | 40 |
| 2nd partial test | 60 |
The minimum global qualification required to pass the subject is 5 points. The student at second time of enrollement can choose either modality 1 or modality 2 of evaluation according to what they deems appropriate.
GENERAL CONSIDERATIONS:
- There will be a final examination for those students who have not approved the subject through modality 1 or 2. In order to participate in this exam the students must have been previously evaluated in a series of activities whose weight equals to a minimum of two thirds of the total grade of the subject. The final exam will include the full year subject and the minimum mark required to pass will be 5 points. At the time the student presents for this exam, it will be considered that the final grade of the subject will be the one that he obtains in this test, regardless of whether he has previously followed modalities 1 or 2 of evaluation.
- Single assessment: Students who request it, following the instructions of the University and the Faculty of Medicine, will have the possibility of being evaluated in a single test. The test will be at the end of the academic year and will include all the syllabus taught throughout the course, both theory and practice, and the minimum grade necessary to pass will be 5 points. The moment students request this kind of examination, it will be considered that the final grade of the subject will be the one obtained in this test. Recovery: The same recovery system will be applied as for thecontinuous evaluation
- It will be considered that a student will obtain the \"Non-Appraising\" qualification if it is solely presented at one of the first two partial tests (T1 or P1) and is not presented to the final exam.
IMPORTANT: The commission of any irregularity in an assessment (academic fraud, plagiarism, or improper use of AI, unless such use is expressly authorized in the course syllabus), which could lead to a significant change in the grade, will result in that attempt being graded with a 0. If the course syllabus stipulates that obtaining a minimum score on this assessment is a mandatory requirement to pass the course, or if multiple irregularities occur in the assessments for the same course, the final grade for that course will be 0. In addition, disciplinary proceedings may be initiated against any student who commits any of these irregularities.
GENERAL NOTE REGARDING THE USE OF AI:
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the work, provided that the final result reflects a significant contribution from the student in the analysis and personal reflection. The student must clearly identify which parts were generated with this technology, specify the tools used, and include a critical reflection on how these have influenced the process and the final result of the activity. A lack of transparency regarding the use of AI will be considered academic dishonesty and may result in a penalty to the activity's grade, or more severe sanctions in more serious cases.
Bibliography
Bibliography
Milton JS. Estadística para biología y ciencias de la salud. 3a. Edición. Madrid: Interamericana. McGraw-Hill, 2001.
Daniel WW. Bioestadística. Base para el análisis de las ciencias de la salud. 4a Edición. Limusa Wiley, 2002.
Cuadras CM. Fundamentos de estadística: aplicación a las ciencias humanas. Barcelona: EUB, 1996.
Sentís J, Pardell H, Cobo E, Canela J. Manual de Bioestadística. 3a. Edición. Barcelona: Masson, 2003.
web links:
https://www.bioestadistica.uma.es/baron/apuntes/
http://www.hrc.es/bioest/M_docente.html
Simulators:
http://onlinestatbook.com/stat_sim/sampling_dist/index.html
Online sample size calculation:
Software
IBM SPSS statistical software, installed on the computer labs in the Medical Library.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (PAUL) Classroom practices | 1 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 1 | Catalan/Spanish | annual | morning-mixed |
| (PAUL) Classroom practices | 2 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 2 | Catalan/Spanish | annual | morning-mixed |
| (PAUL) Classroom practices | 3 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 3 | Catalan/Spanish | annual | morning-mixed |
| (PAUL) Classroom practices | 4 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 4 | Catalan | annual | morning-mixed |
| (PAUL) Classroom practices | 5 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 5 | Catalan | annual | morning-mixed |
| (PAUL) Classroom practices | 6 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 6 | Catalan/Spanish | annual | morning-mixed |
| (PAUL) Classroom practices | 7 | Catalan/Spanish | annual | morning-mixed |
| (PLAB) Practical laboratories | 7 | Catalan/Spanish | annual | morning-mixed |
| (PAUL) Classroom practices | 8 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 8 | Catalan | annual | morning-mixed |
| (PAUL) Classroom practices | 9 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 9 | Catalan | annual | afternoon |
| (PLAB) Practical laboratories | 10 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 11 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 12 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 13 | Catalan | annual | afternoon |
| (PLAB) Practical laboratories | 14 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 15 | Catalan | annual | afternoon |
| (PLAB) Practical laboratories | 16 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 17 | Catalan/Spanish | annual | afternoon |
| (PLAB) Practical laboratories | 18 | Catalan/Spanish | annual | afternoon |
| (TE) Theory | 101 | Catalan/Spanish | annual | afternoon |
| (TE) Theory | 102 | Catalan/Spanish | annual | afternoon |
| (TE) Theory | 103 | Catalan/Spanish | annual | morning-mixed |
| (TE) Theory | 104 | Catalan/Spanish | annual | morning-mixed |