Biostatistics and Data Analysis
Code: 101917 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2501230 Biomedical Sciences | FB | 1 | 2 |
Contact
- Name:
- Mercedes Campillo Grau
- Email:
- mercedes.campillo@uab.cat
Use of Languages
- Principal working language:
- spanish (spa)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- No
- Some groups entirely in Spanish:
- No
Teachers
- Leonardo Pardo Carrasco
Prerequisites
There are no official prerequisites, however prior knowledge of elementary mathematics including the concepts of differentiation and integration is recommended.
Objectives and Contextualisation
Biostatistics and Data Analysis aims to introduce the student to the fundamental knowledge and use of the basic tools of knowledge in accordance with the scientific method.
The course will address issues relating to research in the fields of Biology and Medicine with the mathematical method and, especially, from the theory of probabilities. This approach will allow the precise quantification of significant relationships between the various phenomena related to health and human pathology from the perspective of Biomedical research.
To achieve these objectives, the student must work with various conceptual, methodological and instrumental tools necessary to develop a vision of Biomedicine in accordance with scientific rigor.
Competences
- Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
- Apply knowledge acquired to the planning and implementation of research, development and innovation projects in a biomedical research laboratory, a clinical department laboratory or the biomedical industry.
- Describe biomedical problems in terms of causes, mechanisms and treatments.
- Make changes to methods and processes in the area of knowledge in order to provide innovative responses to society's needs and demands.
- Read and critically analyse original and review papers on biomedical issues and assess and choose the appropriate methodological descriptions for biomedical laboratory research work.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
- Work as part of a group with members of other professions, understanding their viewpoint and establishing a constructive collaboration.
Learning Outcomes
- Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
- Design, plan and interpret different studies in order to tackle public health problems.
- Determine the sample size needed to contrast the hypothesis.
- Distinguish between the different sources of information on health problems.
- Draw up and contrast hypotheses and identify the errors associated with them.
- Estimate population parameters from the corresponding sample parameters.
- Interpret problems and intervention measures in public health.
- Make changes to methods and processes in the area of knowledge in order to provide innovative responses to society's needs and demands.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must be capable of communicating information, ideas, problems and solutions to both specialised and non-specialised audiences.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Take account of social, economic and environmental impacts when operating within one's own area of knowledge.
- Understand and critique scientific articles on statistics.
- Work as part of a group with members of other professions, understanding their viewpoint and establishing a constructive collaboration.
Content
UNIT 1. INTRODUCTION
1.1. Definition and objectives
1.2. Population and sample
1.3. Descriptive statistics, probability theory and inferencial statistics
UNIT 2. MONOVARIANT DESCRIPTIVE STATISTICS
2.1. Quantitative and qualitative variables. Absolute, relative and cumulative frequencies. Graphic representations
2.2. Continuous quantitative variables. Enumerative data: Frecuency tables. Graphic representations. Measures of central tendency: mean, median and mode. Measures of dispersion: range, variance, standard deviation and coefficient of variation. Morphological measures: bias and kurtosis
UNIT 3. BIVARIANT DESCRIPTIVE STATISTICS
3.1. Qualitative relationship between two variables: Contingency tables. Relationship between continuous quantitative and qualitative variables. Relationship between two continuous quantitative variables (correlation coefficient)
3.2. Matching data (repeated measurements)
UNIT 4. PROBABILITY THEORY
4.1. Experiment random sample space and event
4.2. Event operations: union, intersection, difference and contrary events. Incompatible events
4.3. Absolute and relative frequencies. Probability
4.4. Conditional probability. Independent events. Probability of union and intersection of events
4.5. Bayes Theorem
4.6. Measuring the frequency of a disease in the population. Incidence and prevalence
4.7. Evaluation of risk factors. Relative risk and odds ratio
4.8. Evaluation of diagnostic criteria. Sensitivity, specificity, positive and negative predictive values
UNIT 5. RANDOM VARIABLES
5.1. Discrete and continuous random variables
5.2. Probability density function, probability distribution function, expectation and variance of discrete and continuous random variables
5.3. Probability distributions from discrete random variables: Binomial and Poisson
5.4. Probability distributions from continuous random variables: normal, χ2, Student's t and Fisher Snedecor F
5.5. Central Limit Theorem. De Moivre theorem. Sampling distribution. Interval Probability
UNIT 6. ESTIMATION
6.1. Estimation methods: interval confidence. Differences between probability and confidence intervals
6.2. Estimated mean, variance and proportion of population. Determination of the sample size
UNIT 7. HYPOTHESIS TESTING
7.1. Null and alternative hypothesis. Errors type I and type II or α and β risk. One-tailed and two-tailed contrasts. Significance level. Sample Size
7.2. Testing about population mean, population variance and population proportion
7.3. Testing about of differences in mean, variance and proportions. Kolmogorov-Smirnov test. Nonparametric comparison of two samples: Mann-Whitney U test
7.4. Hypothesis testing of paired data. Nonparametric Wilcoxon Signed-Rank test
UNIT 8. RELATIONSHIP BETWEEN QUANTITATIVE AND QUALITATIVE VARIABLES: ANALYSIS OF VARIANCE (ANOVA) AND REGRESSION
8.1. One-way ANOVA. Tests a priori and a posteriori
8.2. Regression: Least squares, significance of the regression and confidence intervals for population parameters. Linearity and utility tests
UNIT 9. RELATIONSHIP BETWEEN TWO RANDOM QUANTITATIVE VARIABLES: CORRELATION
9.1. Correlation Coefficient. Significance of correlation coefficient. Comparison between regression and correlation
UNIT 10. RELATIONSHIP BETWEEN QUALITATIVE VARIABLES: CHI-SQUARE TESTS
10.1. Goodness-of-fit of theoretical distributions frequency distributions
10.2. Homogeneity and independence tests
10.3. McNemar test for paired data
Methodology
Theory lectures:
The lectures will be taught with face-to-face methodology, although the interaction and participation of the students will be made possible and estimulated 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; students are encouraged to print and bring it to class to use as a support when taking notes. The student will also be encouraged to deepen the knowledge acquired in class using the recommended bibliography and simulation software.
Problem classes / Practice seminars:
Given the character and orientation of the subject, the classes of problems, conveniently interspersed with those of theory, will play a key role in its development and in the learning of the subject.
THrough the Virtual Campus collections of problems will be delivered, organized according to the topics of the subject, which the student must develop both in class and individually. Most of these problems will be practical cases that, in solving them, allow the student a greater understanding of the knowledge acquired in the theory classes and in personal study.
In the classes of problems, tools such as Kahoot will also be used for the consolidation of content and as a diagnosis of the knowledge acquired.
In the practical seminars, conveniently interspersed with the theory classes, the methodology and dynamics of the SPSS software (or other statistical package) will be introduced. The student must use them in practical classes in order to achieve the learning object of the subject.
Practical Classes:
The practical classes are a fundamental point for the correct fulfillment of the objectives of the subject. In them the students will have to solve practical cases, previously selected, by means of statistical software.Learning includes both the introduction and manipulation of data, as well as the use of the main facilities offered by this software for data analysis. The practices will be carried out individually or in pairs. The development of these classes will be linked to the theoretical and problems classes with good temporal correlation.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Practical classes | 20 | 0.8 | 1, 14, 15, 2, 4, 5, 8, 16 |
| Seminars and problems classes | 6 | 0.24 | 1, 14, 15, 3, 2, 4, 5, 6, 7, 8, 13, 9, 10, 16 |
| Theory lectures | 24 | 0.96 | 15, 3, 2, 4, 5, 6, 7, 13, 9, 16 |
| Type: Supervised | |||
| Consolidation practices | 7 | 0.28 | 15, 3, 2, 4, 5, 6, 7, 8, 12, 16 |
| Type: Autonomous | |||
| Personal study | 42 | 1.68 | 1, 14, 15, 2, 4, 7, 8, 12, 10 |
| Questionnaires of practices | 7 | 0.28 | 1, 3, 2, 5, 6, 7, 13, 9, 10, 16 |
| Resolution of exercises | 24 | 0.96 | 15, 3, 2, 5, 6, 7, 13, 9, 10 |
| Tests resolution | 10 | 0.4 |
Assessment
The competences of the subject will be evaluated according to the following criteria:
- Multiple-choice tests (with one or more correct answers per question) of conceptual questions and problem solving [T1 tests (30%) and T2 (35%)]
- Practical tests with computer [P1 tests (10%) and P2 (15%)] and,
- Attendance and presentation of practical questionnaires (10%)
| Theoretical exams: | ||
| 1st partial test | T1 | 30% |
| 2nd partial test | T2 | 35% |
| Practical exxams: | ||
| 1st partial test | P1 | 10% |
| 2nd partial test | P2 | 15% |
|
Attendance and presentation of practice questionaries |
10% |
Ratings:
- The minimum overall grade necessary to pass the subject by continuous assessment will be 5 points.
- To average and pass the course by continuous assessment, the minimum score in the theory exams will be 3,0 points.
- If students do not pass the subject by continuous assessment, since they do not reach a minimum of 3,0 points in any of the theory exams, the grade of the course will be 4 points maximum.
- A student will be considered "Non-evaluable" if the set of activities that have been evaluated do not allow him to achieve overall grade of 5, assuming that he had obtained the highest score in all of them.
Recovery Exam (Final):
- There will be a make-up exam, either for those students who have not passed the subject by continuous evaluation, or for those who wish to raise their grade (which implies giving up the grades obtained in the two theoretical exams through continuous evaluation).
- Only students who have been previously evaluated in a set of activities, the weight of which equals a minimum of two thirds of the total grade for the subject, may take the recovery exam.
- The make-up exam will include all the subject, although its result will represent 65% of the final grade, since the remaining 35% will continue to depend on the results of the practical part.
Repeating students:
- From the second registration, students can decide between repeating the practical classes or only conduct the theoretical exams.
- In the latter case the percentage of the tests will be 40% and 60% (for 1st and 2nd partials, respectively) in case of continuous assessment and 100% for the final exam.
Exams revisions:
Following the regulations of the University, the procedure, place, date and time of the exams revision will be announced.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Practical test with computer - 1st partial | 10% | 2 | 0.08 | 1, 14, 15, 3, 5, 6, 13, 12, 11, 9, 10 |
| Practical test with computer - 2nd partial | 15% | 2 | 0.08 | 1, 14, 15, 3, 2, 5, 6, 8, 13, 12, 11, 9, 10 |
| Theoretical and practical questions - 2nd partial | 35% | 3 | 0.12 | 1, 15, 3, 2, 5, 6, 8, 13, 12, 11, 9, 10, 16 |
| Theoretical and practical questions - 1st partial | 30% | 3 | 0.12 | 1, 15, 2, 4, 7, 13, 11, 9, 10, 16 |
Bibliography
Basic bibliography:
Milton JS. Estadística para biología y ciencias de la salud. 3a. Edición. Madrid: Interamericana. McGraw-Hill, 2007. (https://elibro.net/es/lc/uab/titulos/50273).
Taylor RA, Blair RC. Bioestadística. México: Pearson Education, 2008 (https://elibro.net/es/lc/uab/titulos/107439).
Daniel WW. Bioestadística. Base para el análisis de las ciencias de la salud. 4a Edición. México: Limusa Wiley, 2002.
Sentís J, Pardell H, Cobo E, Canela J. Manual de Bioestadística. 3a. Edición. Barcelona: Masson, 2003.
Sorribas A, Abella F, Gómez X, March J. Metodologia estadística en ciències de la salut: Del disseny de l’estudi a l’anàlisi de resultats. Edicions de la Universitat de Lleida i F.V. Libros. 1997.
Ferrán M, SPSS para Windows. Análisis Estadístico. McGraw-Hill, 2001. (https://elibro.net/es/lc/uab/titulos/50036)
Visauta B. Analisis estadístico con SPSS 14. Estadística básica. 3a Edición. McGraw-Hill, 2007. (https://elibro.net/es/lc/uab/titulos/50128)
Martínez-González MA, Sánchez-Villegas A, Toledo E, Faulin FJ. Bioestadística amigable. 4a. Edición. Elsevier. 2020
Web links:
https://www.ibm.com/docs/SSLVMB_27.0.0/pdf/es/IBM_SPSS_Statistics_Brief_Guide.pdf
http://www.hrc.es/bioest/M_docente.html
https://link-springer-com.are.uab.cat/book/10.1007%2F978-3-319-20600-4
http://davidmlane.com/hyperstat/index.html
https://seeing-theory.brown.edu
Simulators:
http://demonstrations.wolfram.com/ - http://demonstrations.wolfram.com/topic.html?topic=Statistics&limit=20
http://socr.ucla.edu/SOCR.html