Biostatistics course: Statistical Analysis applied to Research Data
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Biostatistics course: Statistical Analysis applied to Research Data
Description:
The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.
The course will be composed of face-to-face theoretical lectures combined with practical sessions and additional short homework assignment (approx. 1 h per week).
Teacher: Hafid Laayouni (UPF)
Duration: 10 sessions (total 15h)
Schedule: 26th April- 28th June, Fridays 10:00- 11:45 (additional optional practical sessions will be scheduled on Tuesdays 3-4pm).
Where: CRG Training centre (PRBB patio)
Maximum nº students: 19
Requirements: Participants need to have basic R programing skills
Application deadline: 12th of April 2019
Sessions:
|
Session |
Type |
Title |
Date (Fridays) from 10:00-11:30 |
|
1 |
Theory |
Introduction. Descriptive statistics. Probability and distributions. Sampling distribution. Confidence intervals |
26th April 2019 |
|
2 |
Theory |
Hypothesis testing. The t-test. |
3rd May 2019 |
|
3 |
Practicum |
Exercises and Hands on sessions 1-2. |
10th May 2019 |
|
4 |
Theory |
Analysis of variance. One-way ANOVA |
17th May 2019 |
|
5 |
Theory |
ANOVA. Multiple comparison procedures. Two-way ANOVA. |
24th May 2019 |
|
6 |
Practicum |
Hands on sessions 3-4. |
31st May 2019 |
|
7 |
Theory |
Correlation and regression |
7th June 2019 |
|
8 |
Theory |
Multiple regression analysis. Partial correlation. |
14th June 2019 |
|
9 |
Practicum |
Hands on sessions 7-8 |
21st June 2019 |
|
10 |
Theory |
Analysis of Covariance. Effect Size, and a summary of inference methods. |
28th June 2019 |
At the end of the course, the students will be able to:
- Express a scientific question in a mathematical formulation possible to analyse with statistical method.
- To be confident about sampling methods and their impact on the quality of the data.
- Think on the basis of experimental design: replication, randomization and stratification.
- Think about the appropriate statistical method given the data we have (dependent and/or independent variable, continuous or categorical variables).
- Have a deep understanding of the meaning and interpretation of a test statistic and how to construct own statistic given data.
- Have a correct understanding of key words in basic statistic: null hypothesis, alternative hypothesis, error type I, error type II, statistical power, observational study, experimental study, causation and correlation, sampling error, random error, systematic error, sampling distribution, p-value, statistical and practical significance, effect size, statistical power.
- Correct interpretation of the result of a statistical test and been aware of the limitations of the applied methods.