Objectives
To provide students with the fundamental concepts and techniques of biostatistics and basic epidemiology necessary to design simple studies, analyse biomedical data using computer software and correctly interpret their results, within the framework of evidence-based medicine.
Prerequisites
Recommended: secondary school level knowledge of mathematics.
Competencies
RC13. Be able to design and carry out simple statistical studies using computer software and interpret the results.
RC14. To understand and critically interpret scientific texts.
RC15. Understand and apply the principles of (best) evidence-based medicine.
Learning outcomes
RK39. To be familiar with the basic concepts of biostatistics and their application to the medical sciences.
RS8. Understand and interpret statistical data in the medical literature.
RS9. Use a personal computer independently.
RS12. Present scientific work and/or professional reports in public, both orally and in writing.
Course description
A core module covering compulsory core content on statistical methods, applied statistics, basic epidemiology and evidence-based medicine. Students must acquire skills in introductory research, including information sources, new technologies and databases.
KNOW:
1. Understand the basic concepts of biostatistics and their applicability to the biomedical sciences.
2. Design and analyse simple studies.
3. Understand the main statistical techniques and their application.
4. Understand the usefulness and applications of statistical software.
5. Distinguish between a sample and a population, as well as between random and non-random samples.
6. To analyse a dataset descriptively.
7. Understand the most important distributions and know when to use them.
8. Construct and interpret confidence intervals for means and proportions.
9. Formulate the hypotheses for a hypothesis test based on the aim of the experiment, and understand their limitations and interpretation.
10. Understand the relationship between confidence intervals and hypothesis testing.
11. Interpret the p-value and draw conclusions.
12. Distinguish between independent and paired samples.
13. Distinguish between parametric and non-parametric methods.
14. Test one or two means or proportions depending on the type and number of data points.
15. Understand the applications of the chi-squared test, recognise the limitations of the technique and analyse the reasons for its significance.
16. Understand the concepts of risk factor, relative risk, odds ratio and aetiological fraction.
17. The problem of multiple comparisons and the Bonferroni correction.
18. Understand the concept and interpretation of simple linear regression and correlation studies.
19. Understand the concept and interpretation of survival analysis. The Kaplan–Meier method.
20. Understand the concept, purpose and design of a clinical trial. Meta-analysis.
21. Evidence-based medicine.
SKILLS:
1. Interpret the levels of precision, confidence and error in the conclusions of a statistical study.
2. Use a statistical software package at a user level.
3. Analyse data descriptively.
4. Calculate confidence intervals for means and proportions, determining the appropriate sample size.
5. Formulate the hypotheses for a hypothesis test.
6. Calculate the p-value.
7. Compare two means.
8. Compare two proportions.
9. Analyse a contingency table using the chi-squared test.
10. Apply the Bonferroni method.
11. Plot a scatter plot, regression line and correlation coefficient for two variables.
12. Calculate Spearman’s correlation coefficient.
13. Create a database.
14. Use a personal computer and the most commonly used medical software independently.
To have practised the following under the supervision of a tutor:
1. Break down contingency tables to identify the reasons for the significance of a chi-squared test.
2. Transform variables to ensure they meet the specifications of the model to be used.
3. Obtain confidence intervals for normality in linear regression.
4. Calculate the appropriate measure in an epidemiological study.
5. Evaluate a binary diagnostic method. Perform exact tests for two proportions.
6. Compare several means using parametric and non-parametric methods.
7. Perform multiple regression.
8. Use a personal computer independently and operate the most commonly used medical software.
Contents:
BLOCK I. INTRODUCTION AND DESCRIPTIVE STATISTICS
˗ UNIT 1: The scientific and statistical method. Clinical trials and meta-analyses.
˗ UNIT 2: Population and sample. Characteristics. Classification and description of traits. Types of variables.
˗ UNIT 3: Questionnaires: variables and measurement scales. Clinical measurement.
˗ UNIT 4: Qualitative variables: frequency distributions and graphical representation.
˗ UNIT 5: Quantitative variables: measures of central tendency, position, dispersion and shape. Graphical representation.
BLOCK II. PROBABILITY
˗ UNIT 6: Probability and the assignment of probabilities.
˗ UNIT 7: Conditional probability. Bayes’ theorem. Clinical diagnosis. Sensitivity, specificity and ROC curves.
BLOCK III. PROBABILITY DISTRIBUTIONS
˗ UNIT 8: Random variables: probability function, density and distribution.
˗ UNIT 9: Notable discrete distributions: Binomial and Poisson.
˗ UNIT 10: Notable continuous distributions: the normal distribution and its applications. Approximation between distributions.
BLOCK IV. ESTIMATION AND CONFIDENCE INTERVALS.
˗ UNIT 11: General outline of statistical inference: sampling, bias and the Central Limit Theorem. Statistical tests.
˗ UNIT 12: Estimation of a mean: point estimate and confidence interval. Sample size.
˗ UNIT 13: Estimation of a proportion: confidence interval. Sample size.
BLOCK V. HYPOTHESIS TESTING. COMPARISONS AND MODELS.
- UNIT 14: Introduction to hypothesis testing. Testing the mean of a population.
- UNIT 15: Testing the means of two populations.
- UNIT 16: Comparison of two means: independent and paired samples. Parametric and non-parametric tests.
- UNIT 17: Comparison of two proportions. Contingency tables: chi-squared test. Bonferroni correction and statistical power.
˗ UNIT 18: Linear regression, correlation and ANOVA.
- UNIT 19: Evidence-Based Medicine.
Training activities
1. Lectures: Explanation of the theoretical foundations of statistical methods, applied statistics and basic epidemiology, using computer tools and audiovisual resources. 45 hours (45 face-to-face).
2. Specialised seminars: Solving exercises and applied problems based on the content covered in the lectures, with active student participation and joint discussion of the results. Presentation, study and discussion of case studies or projects. Presentation of projects. Discussion of popular science articles. 10 hours (10 face-to-face).
3. Laboratory sessions: Use of statistical software for the descriptive and inferential analysis of data sets, under the supervision of teaching staff. 10 hours (10 face-to-face).
4. Individual tutorials: Personalised sessions, by appointment, to address queries and monitor the student’s learning progress. 1 hour (1 face-to-face session).
5. Exams: Assessments of theoretical content and laboratory practicals. 4 hours (4 face-to-face).
6. Independent study: Study of theoretical and practical content, completion of additional exercises and consultation of the recommended reading list. 105 hours (0 face-to-face).
Total: 175 hours (70 face-to-face).
Assessment system and criteria
SE1: Objective assessment by teaching staff via the final course examination and a mid-term examination: 70%
SE2: Assessment of the student’s attendance and participation in class: 5%
SE3: Assessment of laboratory practicals via an examination or submission of practical work: 15%
SE4: Assessment of individual and/or group assignments through submission and/or presentation of work: 10%
Timetable
Click on this link to view the detailed timetable in Excel
| Session |
Activity |
Description |
Assessment |
| 1 |
MG |
Course introduction |
|
| 2 |
MG |
UNIT 1: Scientific and statistical methods. Clinical trials and meta-analyses. |
|
| 3 |
MG |
UNIT 2: Population and sample. Characteristics. Classification and description of traits. Types of variables. |
|
| 4 |
MG |
UNIT 2: Population and sample. Characteristics. Classification and description of traits. Types of variables. |
|
| 5 |
MG |
UNIT 3: Questionnaires: variables and measurement scales. Measurement in clinical practice. |
|
| 6 |
MG |
UNIT 3: Questionnaires: variables and measurement scales. Clinical measurement. |
|
| 7 |
MG |
UNIT 4: Qualitative variables: frequency distributions and graphical representation. |
|
| 8 |
MG |
UNIT 4: Qualitative variables: frequency distribution and graphical representation. |
|
| 9 |
MG |
Revision |
|
| 10 |
MG |
UNIT 5: Quantitative variables: measures of central tendency, position, dispersion and shape. Graphical representation. |
|
| 11 |
MG |
UNIT 5: Quantitative variables: measures of central tendency, position, dispersion and shape. Graphical representation. |
|
| 12 |
SM |
Exercises I: Solving descriptive statistics exercises. |
|
| 13 |
MG |
UNIT 6: Probability and probability distribution. |
|
| 14 |
MG |
UNIT 6: Probability and probability distribution. |
|
| 15 |
MG |
UNIT 7: Conditional probability. Bayes’ theorem. Clinical diagnosis. Sensitivity, specificity and ROC curves. |
|
| 16 |
MG |
UNIT 7: Conditional probability. Bayes’ theorem. Clinical diagnosis. Sensitivity, specificity and ROC curves. |
|
| 17 |
MG |
UNIT 8: Random variables: probability function, density and distribution. |
|
| 18 |
SM |
Exercises II: Solving descriptive statistics exercises |
|
| 19 |
MG |
UNIT 9: Notable discrete distributions: Binomial and Poisson. |
|
| 20 |
MG |
UNIT 9: Notable discrete distributions: Binomial and Poisson. |
|
| 21 |
MG |
UNIT 8: Random variables: probability function, density and distribution. |
|
| 22 |
MG |
UNIT 10: Notable continuous distributions: the normal distribution and its applications. Approximation between distributions. |
|
| 23 |
SM |
Exercises III: Solving probability problems |
|
| 24 |
MG |
Mid-term exam (covering content up to Unit 6) |
|
| 25 |
MG |
UNIT 10: Notable continuous distributions: the normal distribution and its applications. Approximation between distributions. |
|
| 26 |
MG |
UNIT 11: General outline of inference: sampling, bias and the Central Limit Theorem. |
|
| 27 |
MG |
UNIT 11: General framework of inference: sampling, bias and the Central Limit Theorem. |
|
| 28 |
MG |
UNIT 13: Estimating a proportion: confidence interval. Sample size. |
|
| 29 |
MG |
UNIT 12: Estimating a mean: point estimate and confidence interval. Sample size. |
|
| 30 |
SM |
Exercises IV: Solving exercises on probability distributions |
|
| 31 |
MG |
UNIT 13: Estimating a proportion: confidence interval. Sample size. |
|
| 32 |
MG |
UNIT 14: Introduction to hypothesis testing. Testing the mean of a population. |
|
| 33 |
MG |
UNIT 14: Introduction to hypothesis testing. Testing the mean of a population. |
|
| 34 |
MG |
UNIT 12: Estimating a mean: point estimate and confidence interval. Sample size |
|
| 35 |
MG |
UNIT 14: Introduction to hypothesis testing. Testing the mean of a population. |
|
| 36 |
SM |
Exercises V: Solving exercises on probability distributions |
|
| 37 |
MG |
UNIT 15: Comparing the means of two populations |
|
| 38 |
MG |
UNIT 15: Comparing the means of two populations |
|
| 39 |
MG |
UNIT 19: Evidence-Based Medicine |
|
| 40 |
MG |
UNIT 16: Comparison of two means: independent and paired samples. Parametric and non-parametric tests. |
|
| 41 |
MG |
UNIT 16: Comparison of two means: independent and paired samples. Parametric and non-parametric tests. |
|
| 42 |
SM |
Exercises VI: Solving exercises on probability distributions |
|
| 43 |
MG |
UNIT 16: Comparing two means: independent and paired samples. Parametric and non-parametric tests. |
|
| 44 |
SM |
Exercises VII: Solving hypothesis testing exercises |
|
| 45 |
LB |
Descriptive statistics and conditional probability. Contingency tables. |
|
| 46 |
LB |
Confidence intervals and sample sizes. |
|
| 47 |
LB |
Association of qualitative variables: hypothesis testing and quantitative variables. |
|
| 48 |
MG |
UNIT 17: Comparison of two proportions. Contingency tables: chi-squared test. Bonferroni correction. |
|
| 49 |
LB |
Association between quantitative variables. Assessment of practicals (submission of practicals) |
15 |
| 50 |
MG |
UNIT 17: Comparison of two proportions. Contingency tables: chi-squared test. Bonferroni correction. |
|
| 51 |
MG |
UNIT 17: Comparison of two proportions. Contingency tables: chi-squared test. Bonferroni correction. |
|
| 52 |
MG |
UNIT 18: Linear regression, correlation and ANOVA. |
|
| 53 |
MG |
UNIT 18: Linear regression, correlation and ANOVA. |
|
| 54 |
MG |
UNIT 18: Linear regression, correlation and ANOVA. |
|
| 55 |
SM |
Exercises VIII: Solving hypothesis testing exercises |
|
| 56 |
MG |
UNIT 18: Linear regression, correlation and ANOVA. |
|
| 57 |
MG |
Revision |
|
| 58 |
SM |
Exercises IX: Solving hypothesis testing exercises. Survival analysis. |
|
| 59 |
SM |
Exercises X: Solving final exercises and addressing queries. Submission and presentation of individual and/or group work |
10 |
| 60 |
CN |
Tutorial to monitor the learning process (date to be confirmed, at the student’s request). Assessment of attendance and participation |
5 |
| 61 |
EV |
Final examination for the module (official date as per the examination timetable; not included in the teaching plan) |
70 |
Bibliography
Core:
1. Martínez González, Miguel Ángel, editor; Sánchez-Villegas, Almudena; Toledo Atucha, Estefanía; Faulín Fajardo, Francisco Javier
Biostatistics Made Easy
5th ed. Elsevier. 2025.
ISBN: 9788413829821
http://sapiens.uax.com/discovery/fulldisplay?docid=alma99922055803591&context=L&vid=34UAX_INST:34UAX_V1&lang=es&search_scope=MyInst_and_CI&adaptor=Local%20Search%20Engine&tab=Everything&query=any,contains,biostatistics&sortby=date_d&facet=frbrgroupid,include,9001610310540091269&offset=0