FAR8001 - NFIF - Statistical Modeling and Multivariate Analysis of Multidimensional Data Sets
The course will offer training opportunity in applied, advanced statistics, combining statistical modelling and multivariate analyses. With its balance between statistical theory and application, the course focus on analysis of real multidimensional data obtained from complex designs, and stress on effective communication of quantitative results in reports, publications and oral presentations, and will provide a thorough introduction to advanced statistical applications for young researchers in the health sciences.
After a brief review of study design and data analysis in biomedical and pharmaceutical research, the course will cover statistical modelling (Maximum Likelihood models), classification and ordination methods. The multivariate methods covered will include cluster analysis, discriminant analysis, multidimensional scaling (MDS), Principal Component Analysis (PCA), Correspondence Analysis (CA), and Constrained Correspondence Analysis (CCA). Graphical presentation and interpretation will play a prominent role to ensure exposure to effective communication of quantitative results.
After completing the course the student should:
- Know how to design and perform complex quantitative studies.
- Know how to clearly and effectively communicate the results of the analyses of complex studies with the help of graphical representations.
- Know how to interpret statistical modelling and multivariate analysis results and draw correct inferences from these.
- Be able to choose appropriate statistical methods based on problem and character of the data.
- Be able to implement correctly the chosen statistical methods (with the help of suitable software, e.g. R).
- Be able to report results of complex quantitative studies.
- Understand the relevant methodological literature, including new advancements.
- Be able to critically evaluate complex quantitative studies.
- Be able to plan, perform and report complex quantitative studies.
For information about how to apply for admission see here.
Applicants, who are affiliated with the national network National PhD School of Pharmacy, will be prioritised for admission if the number of applicants exceeds the course capacity of 25 students.
To take PhD courses you need at a minimum a master's degree or equivalent, or admission to a Medical Student Research Program.
Course attendants are expected to be familiar with descriptive statistics (figures and summary tables) and basic inferential statistics (estimation and confidence intervals, hypothesis testing and P-values). Attendants should also have some experience handling databases (in e.g. Excel) and using statistical software. During the course we will use the software R, exposure to this program helps but is not required to participate.
The course is intensive, with mandatory lectures (15 h) and pc-labs (20 h) concentrated in one week. In preparation for the course, attendants will be asked to read background literature and prepare for individual presentations of their PhD research and for pc-lab activities. During the course, attendants will present their PhD work and summarize the research that forms the basis for the written report (semesteroppgave).
Lectures and and pc-lab are mandatory.The course attendants will have to prepare a short presentation of their research outlining i) research question(s), ii) aims, iii) approach (emphasis on quantitative aspect, i.e. study design and planned statistical analyses), preliminary results and interpretation of findings.
Raul Primicerio, UiT
Lars Småbrekke, UiT
Written home examination (semesteroppgave), max. 10 pages. The submission deadline for the written report will be 6 weeks after the last lecture.
In the event that an exam is evaluated as not passed, there will be an opportunity to submit a revised exam paper at the beginning of the next semester. Application deadline for the continuation exam is August 15th.
Grades are awarded on a pass/fail scale.
The course is organised by the Department of Pharmacy at UiT in cooperation with the National PhD School in Pharmacy.
Raul Primicerio, UiT