Methods of Analysis in Health Sciences
Code: 104414Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Computational Mathematics and Data Analytics | OP | 4 |
Contact lecturer
- Name :
- Jose Barrera Gomez
- Email :
- jose.barrera@uab.cat
Group languages
You can consult this information at the end of the document.
Prerequisites
The student is supposed to:
- Be familiar with the binomial and the normal distributions,
- Be able to fit and interpret linear regression models,
- Be able to work with R and LaTeX.
Objectives
The main aims of the course are:
- Learn about the main types of study designs in the field of Epidemiology.
- Learn about the potential impact of both missing data and error measurement on the results of a statistical analysis.
- Learn about the main indicators to measure the presence of a disease or an exposure.
- Learn about the main indicators to measure the association between exposure and disease, specially in the case where both exposure and outcome are binary.
- Be able to identify the appropriate statistical tools for the assessment of the association between a given exposure (potential risk or protective factor) and a given health outcome, according to the characteristics of the study design, in the context of epidemiological studies.
- Learn about the design and implementation of an exact test according to the study design.
- Learn about the design and implementation of simulation studies related to concepts such as empirical power or sample size calculation.
- Be able to search scientific papers using PubMed efficiently.
- Get familiar with the reading of scientific papers.
- Be able to apply the concepts studied in the subject to solve exercises based in true epidemiological data.
- Improve the efficiency when programming in R to solve the practical tasks proposed during the course.
- Be able to write reproducible statistical reports using LaTeX and the R package knitr.
Learning outcomes
- CM34 (Propose suitable statistical models for epidemiological studies.) Propose suitable statistical models for epidemiological studies.
- CM35 (Write technical reports that clearly express the results and conclusions of a bioscience study using vocabulary specific to the field of application.) Write technical reports that clearly express the results and conclusions of a bioscience study using vocabulary specific to the field of application.
- KM29 (Recognise the most used statistical inference methods in bioinformatics.) Recognise the most used statistical inference methods in bioinformatics.
- KM30 (Identify the use of statistical knowledge in bioinformatics and in health science.) Identify the use of statistical knowledge in bioinformatics and in health science.
- KM31 (Identify the most used statistical inference methods in epidemiology studies.) Identify the most used statistical inference methods in epidemiology studies.
- SM36 (Analyse data corresponding to epidemiological studies or clinical trials.) Analyse data corresponding to epidemiological studies or clinical trials.
- SM38 (Use the most common databases in the field of health science.) Use the most common databases in the field of health science.
Contents
*
1. Introduction to the contents. Introduction to reproducible research using the R package knitr.
2. PubMed: Searching scientific papers. Structure of a paper.
3. Classification of studies
(a) Topics in biostatistics
(b) Epidemiological studies
i. Notation
ii. Classification criteria
iii. Types of epidemiological study design: Randomised epidemiological trials, Cohort, Case-control, Case-crossover, Cross-sectional, Ecological
(c) Studies classification diagram
4. Classification of variables and related regression models
(a) According to the measure type
(b) According to the role in the study
(c) Types of explanatory variables
(d) Types of regression models according to the metric of the response variable
(e) Response variables of type time
5. Dealing with missing data
(a) Introduction
(b) Types of missing data
(c) Dealing with missing data
6. Example of statistical methods in Health Sciences: Integration of multiple imputation in cluster analysis
(a) Overview of cluster analysis
(b) Overview of multiple imputation
(c) Integration of multiple imputation in cluster analysis
(d) Software
7. Measures of disease presence
(a) Introduction
(b) Prevalence
i. Definition
ii. Estimation
iii. Comments
(c) Cumulative incidence
i. Definition
ii. Comments
(d) Incidence rate
i. Definition
ii. Comments
iii. Comparing two incidence rates
8. Measures of association between exposure and disease
(a) Introduction
(b) The relative risk
i. Definition
ii. Comments
(c) The odds ratio
i. The odds
ii. The odds ratio
iii. Comments
(d)Confidence intervals for OR and RR
(e) The attributable risk
i. Population attributable risk
ii. Exposureattributable risk
9. Causality, confusion and interaction
(a) Introduction
(b) Causality
(c) Confusion
(d) Interaction
10. Example of statistical methods in Health Sciences: Regression models with transformed variables. Interpretation and software
(a) Overview of the linear regression model
(b) Logarithm transformation in linear regression models. Why?
(c) Interpretation of results in the original scale of the variables
(d) Software
* Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Personal work | 94 | 3.76 | |
| Theory sessions | 28 | 1.12 | |
| Practice sessions | 28 | 1.12 |
*
- Theory sessions: In these sessions, the different concepts of the subject as well as illustrative examples are introduced. Also, some exercises are proposed to be solved (usually requiring R usage). The methodology is based in the presentation and discussion of slides as well as the presentation of some additional materials (mainly news published in online media and scientific papers searched in PubMed).
- Practice sessions: In these sessions, several practical examples and exercises will be proposed. Activities related to R usage, PubMed search, papers reading and statistical analyses will be developed. Some of the proposed exercises will be mandatory.
- Seminars attendance: The Department of Mathematics and the UAB Statistical Service organize statistical seminars. The students and the teacher would attend some of them, depending on the topic and the schedule.
*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Use of Artificial Intelligence (AI) Technologies:
For this course, a restricted use of Artificial Intelligence (AI) technologies is permitted. Specifically, the use of AI is allowed exclusively for the following tasks:
- Literature or information searching
- Linguistic correction and/or translation of texts
- Assistance with LaTeX code
- Reviewing R code previously created by the student without the use of AI
- Tasks for which the professor explicitly permits, recommends, or mandates the use of AI
For each and every gradable written assignment submitted, the student (or group of students) must clearly identify which parts were generated using AI and, for each of these parts, must specify:
- Name and version of the AI tool
- Prompt (the question or questions asked to the AI)
- Critical reflection on how the use of the AI tool influenced both the process and the final outcome of the activity
Lack of transparency regarding the use of AI in these gradable activities will be considered a breach of academic honesty and may lead to a partial or total grade penalty for the activity, or more severe sanctions in serious cases.
In general, committing any irregularity in any of the assessment activities (academic fraud, plagiarism, or misuse of AI, unless such use is expressly authorized in the course guide) that could lead to a significant variation in the grade will result in a score of 0 for that specific activity. If the course guide states that achieving a minimum grade in that specific assessment activity is an essential requirement to pass the course, or if multiple irregularities occur across the course's assessment activities, the final grade for the course will be 0. Independent of this, disciplinary proceedings may be initiated against any student who commits such irregularities.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Assignments in group | 30% | 0 | 0 | CM34, CM35, KM29, KM30, KM31, SM36, SM38 |
| Exercises in group | 20% | 0 | 0 | CM34, CM35, KM29, KM30, KM31, SM36, SM38 |
| Exam (or compensatory exam) | 50% | 0 | 0 | CM34, KM30, KM31, SM36 |
*
- Assiignments in grup during the course. Teacher could assess individual participation with oral questions.
- Exam (face-to-face).
- Optional compensatory exam (face-to-face). To participate in the compensatory exam, students must have previously been assessed in a set of activities whose weight is equivalent to a minimum of two-thirds of the subject's total grade. He or she must also have obtained a minimum grade of 3.5 out of 10 in the average of the subject. If the student attend the compensatory exam, its qualification will substitute the score in the previous, ordinary exam, regardless of the score obtained in bothe exams.
- The final scoring of the course out of 10, Q, will be:
Q = min{T, E}, if T is less than 4 or E is less than 3.5,
Q = (T + E) / 2, if T is greater than or equal to 4 and E is greater than or equal to 3.5,
on T i E are the scoring, out of 10, of the assignments and the exam, respectively.
- This subject does not offer the possibility of a single assessment (i.e. \"evaluación única\").
* Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Use of Artificial Intelligence (AI) Technologies:
For this course, a restricted use of Artificial Intelligence (AI) technologies is permitted. Specifically, the use of AI is allowed exclusively for the following tasks:
- Literature or information searching
- Linguistic correction and/or translation of texts
- Assistance with LaTeX code
- Reviewing R code previously created by the student without the use of AI
- Tasks for which the professor explicitly permits, recommends, or mandates the use of AI
For each and every gradable written assignment submitted, the student (or group of students) must clearly identify which parts were generated using AI and, for each of these parts, must specify:
- Name and version of the AI tool
- Prompt (the question or questions asked to the AI)
- Critical reflection on how the use of the AI tool influenced both the process and the final outcome of the activity
Lack of transparency regarding the use of AI in these gradable activities will be considered a breach of academic honesty and may lead to a partial or total grade penalty for the activity, or more severe sanctions in serious cases.
In general, committing any irregularity in any of the assessment activities (academic fraud, plagiarism, or misuse of AI, unless such use is expressly authorized in the course guide) that could lead to a significant variation in the grade will result in a score of 0 for that specific activity. If the course guide states that achieving a minimum grade in that specific assessment activity is an essential requirement to pass the course, or if multiple irregularities occur across the course's assessment activities, the final grade for the course will be 0. Independent of this, disciplinary proceedings may be initiated against any student who commits such irregularities.
Bibliography
Basic: All concepts developed in the class sessions will be published at Moodle, including the slides that will be discussed in the theory sessions.
Further readings: Students interested in going further can explore the following items.
- Agresti, Alan. Categorical Data Analysis. Wiley, 3rd Edition, 2013.
- Breslow, N., N. Day. Statistical methods in cancer research. International Agency for Research on Cancer, 1980.
- Clayton D., Hills, M. Statistical models in epidemiology. Oxford University Press, 1993.
- Dalgaard, P. Introductory Statistics with R. Springer, 3rd Edition, 2002.
- dos Santos, I. Cancer epidemiology: principles and methods. International Agency for Research on Cancer, 1999.
- Gordis, L. Epidemiology. W.B. Saunders, 2004.
- Lachin, J.M. Biostatistical Methods: The Assessment of Relative Risks. Wiley, 2000.
- Motulsky, H.J. Intuitive Biostatistics. Oxford University Press, 1995.
- Rothman, K., Greenland, S. Modern epidemiology. Lippincott Williams & Wilkins, 1998.
- Rothman, K. Epidemiology: an introduction. Oxford University Press, 2002.
- Wassertheil-Smoller, S. Biostatistics and epidemiology: a primer for health and biomedical prefessionals. Springer, 3rd Edition, 2004.
Software
- R
- LaTeX
- RStudio
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