Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.R.1.00

Description Period: 2025./26 academic year rudens semestris

Study Course Accepted: -

Study Course Description

Methods of Mathematical Statistics in Health Sciences II

Main Study Course Information

Course Code
SL_044
Branch of Science
Mathematics; Theory of probability and mathematical statistics
ECTS
3.00
Target Audience
Medicine; Pharmacy
LQF
Level 8
Study Type And Form
Full-Time

Study Course Implementer

Course Supervisor
Māra Grēve
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts

14 Balozu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab

About Study Course

Objective

To provide in-depth knowledge of mostly used statistical methods in health sciences and to offer an insight in proper form for reporting results of statistical analysis.

Preliminary Knowledge

Successfully completed course “Methods of Mathematical Statistics in Health Sciences I”.

Learning Outcomes

Knowledge

1.On successful completion of the study course, students will have knowledge that will allow to recognize benefits of various statistical analysis methods and to characterize measurement data using statistical indicators.

Skills

1.On successful completion of the study course, students will be able to combine various statistical methods with the aim to create valid conclusions about data and use suitable reflection tools for statistical analysis in the description of the results.

Competences

1.On successful completion of the study course, students will be able to justify the choice of statistical methods for data analysis and critically evaluate statistical information given in scientific publications.

Assessment

Individual work

Title
% from total grade
Grade
1.

Individual work
-
-
1. Reading literature from the list of Required reading according to topics of lectures and classes. 2. Reviewing examples of statistical analysis results report forms in scientific publications. 3. Recognize the statistical data analysis situations discussed in the classes in one’s own research data.

Examination

Title
% from total grade
Grade
1.

Examination
-
-
Solved tasks while working individually or in groups (100%).

Study Course Theme Plan

FULL-TIME
Part 1

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
4

Topics

Correlation and association analysis. Insights into regression analysis. Differences between the two methods.
Description
Annotation: During the class, guidelines for choosing statistical data analysis methods will be given and issues of descriptive statistics and visualisation of data discussed. IBM SPSS Statistics or R will be used for practical work to implement the gained knowledge. Topics covered during the class: 1. The summary of data analysis methods used in statistics. 2. Issues of descriptive statistics. 3. Issues of data visualisation. Literature: 1. Mishra, P., Pandey, C. M., Singh, U., Keshri, A., and Sabaretnam, M. 2019. Selection of Appropriate Statistical Methods for Data Analysis. Annals of Cardiac Anaesthesia. 22(3): 297–301. DOI: 10.4103/aca.ACA_248_18 2. Mishra, P., Pandey, C. M., Singh, U. and Gupta, A. 2018. Scales of Measurement and Presentation of Statistical Data. Ann Card Anaesth. 21(4): 419–422. DOI: 10.4103/aca.ACA_131_18 3. Spriestersbach, A., Röhrig, B., du Prel, J-B., Gerhold-Ay, A., and Blettner, M. 2009. Descriptive Statistics. The Specification of Statistical Measures and Their Presentation in Tables and Graphs. Part 7 of a Series on Evaluation of Scientific Publications. Dtsch Arztebl Int. 106(36): 578–583. DOI: 10.3238/arztebl.2009.0578 4. Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2019. ISBN: 978-1-119-16781-5

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
4

Topics

Practical tasks. How to navigate statistical tests? Formalization of the research question
Description
Annotation: During the class, issues with inferential statistics will be discussed: interpretation of p-value, the choice of statistical tests for qualitative and quantitative data, interpretation of results, combining descriptive and inferential statistics. IBM SPSS Statistics or R will be used for practical work to implement gained knowledge. Topics covered during the class: 1. Issues of descriptive and inferential statistics for analysis of quantitative data. 2. Supplementing descriptive statistics with inferential statistics: challenges. 3. Principles of proper form for reporting results of statistical analysis. Literature: 1. Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, Sage Publications, 2013. ISBN-13: 978-1446249185 2. Torgo L. Data Mining with R: Learning with Case Studies, 2nd edition. Chapman and Hall/CRC, 2020. ISBN: 9780367573980 3. Amrhein, V., Greenland, S., McShane, B. 2019. Scientists rise up against statistical significance. Nature. 567(7748):305-307. DOI: 10.1038/d41586-019-00857-9.

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
4

Topics

Introduction to the theory and practice of Analysis of Covariance (ANCOVA and Partial Correlation)
Description
Annotation: During the class mostly used types of regression analysis in health science will be discussed: types, advantages, procedure of checking assumptions, and explaining results. IBM SPSS Statistics or R will be used for practical work to implement gained knowledge. Topics covered during the class: 1. Types of regression analysis and the use in health sciences. 2. Procedure of checking assumptions for regression analysis. 3. Explaining results of regression analysis: examples. Literature: 1. Field A. Discovering Statistics using IBM SPSS Statistics, 4th edition, Sage Publications, 2013. ISBN-13: 978-1446249185 2. Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2019. ISBN: 978-1-119-16781-5 3. Sperandei, S. 2014. Understanding logistic regression analysis. Biochem Med (Zagreb). 24(1): 12–18. DOI: 10.11613/BM.2014.003

  1. Class/Seminar

Modality
Location
Contact hours
On site
Computer room
4

Topics

Research design development based on data analysis methods. Practical tasks individually and in groups. Working with student data or databases.
Description
Annotation: During the class principles of survival analysis and Cox regression will be discussed: advantages, checking assumptions, and explaining results. IBM SPSS Statistics or R will be used for practical work to implement gained knowledge. Topics covered during the class: 1. Advantages of survival analysis and Cox regression. 2. Procedure of checking assumptions. 3. Explaining results: examples. Literature: 1. Zwiener, I., Blettner, M. and Hommel, G. 2011. Survival Analysis. Part 15 of a Series on Evaluation of Scientific Publications. Dtsch Arztebl Int. 108(10): 163–169. DOI: 10.3238/arztebl.2011.0163 2. Petrie A. & Sabin C. Medical Statistics at a Glance, 4th edition, Wiley-Blackwell, 2019. ISBN: 978-1-119-16781-5 3. Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal, 2nd edition, John Wiley & Sons, 2014. ISBN-13: 978-1118589939
Total ECTS (Creditpoints):
3.00
Contact hours:
16 Academic Hours
Final Examination:
Exam

Bibliography

Required Reading

1.

Petrie, A., Sabin, C. Medical Statistics at a Glance. 4th edition, Wiley-Blackwell, 2020.Suitable for English stream

2.

Peat, J., Barton, B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition, John Wiley & Sons, 2014. (jaunāks izdevums nav iznācis)Suitable for English stream

3.

Field, A. Discovering Statistics using IBM SPSS Statistics. Sage Publications, 2024.Suitable for English stream

4.

Torgo, L. Data Mining with R: Learning with Case Studies. 2nd edition. Chapman and Hall/CRC, 2020.Suitable for English stream

Additional Reading

1.

Mishra, P., Pandey, C. M., Singh, U., Keshri, A., and Sabaretnam, M. 2019. Selection of Appropriate Statistical Methods for Data Analysis. Annals of Cardiac Anaesthesia. 22(3): 297–301. DOI: 10.4103/aca.ACA_248_18Suitable for English stream

2.

Mishra, P., Pandey, C. M., Singh, U. and Gupta, A. 2018. Scales of Measurement and Presentation of Statistical Data. Ann Card Anaesth. 21(4): 419–422. DOI: 10.4103/aca.ACA_131_18Suitable for English stream

3.

Spriestersbach, A., Röhrig, B., du Prel, J-B., Gerhold-Ay, A., and Blettner, M. 2009. Descriptive Statistics. The Specification of Statistical Measures and Their Presentation in Tables and Graphs. Part 7 of a Series on Evaluation of Scientific Publications. Dtsch Arztebl Int. 106(36): 578–583. DOI: 10.3238/arztebl.2009.0578Suitable for English stream

4.

Sperandei, S. 2014. Understanding logistic regression analysis. Biochem Med (Zagreb). 24(1): 12–18. DOI: 10.11613/BM.2014.003Suitable for English stream

5.

Amrhein, V., Greenland, S., McShane, B. 2019. Scientists rise up against statistical significance. Nature. 567(7748):305-307. DOI: 10.1038/d41586-019-00857-9Suitable for English stream

6.

Zwiener, I., Blettner, M. and Hommel, G. 2011. Survival Analysis. Part 15 of a Series on Evaluation of Scientific Publications. Dtsch Arztebl Int. 108(10): 163–169. DOI: 10.3238/arztebl.2011.0163Suitable for English stream

Other Information Sources

1.

Laerd statistics.Suitable for English stream

2.

Praktiskā biometrija.

3.

Statistics How To.Suitable for English stream