.
Biostatistics
Study Course Description
Course Description Statuss:Approved
Course Description Version:5.00
Study Course Accepted:06.08.2024 10:44:24
| Study Course Information | |||||||||
| Course Code: | SL_007 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Clinical Pharmacy | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Vinita Cauce | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 23 Kapselu street, 2nd floor, Riga, +371 67060897, statistika rsu[pnkts]lv, www.rsu.lv/statlab | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 8 | Class Length (academic hours) | 3 | Total Contact Hours of Classes | 24 | ||||
| Total Contact Hours | 24 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school knowledge in mathematics and informatics. | ||||||||
| Objective: | To provide students with the opportunity to acquire basic knowledge and skills in statistical data processing methods (descriptive statistics, inferential statistics methods for evaluating differences and analytical statistics), which are necessary for the development of scientific research work and the application of statistical indicators in their specialty. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel. Introduction to IBM SPSS. | Classes | 1.00 | computer room | |||||
| 2 | Descriptive statistics for quantitative and qualitative data. Descriptive statistics of the Normal distribution. Creation of tables and diagrams, correct design. | Classes | 1.00 | computer room | |||||
| 3 | Hypothesis testing. Parametric and nonparametric tests for quantitative data. | Classes | 1.00 | computer room | |||||
| 4 | Hypothesis testing. Tests for qualitative data. | Classes | 1.00 | computer room | |||||
| 5 | Correlation analysis in MS Excel and IBM SPSS. | Classes | 1.00 | computer room | |||||
| 6 | Regression analysis. ROC curves. | Classes | 0.50 | computer room | |||||
| 7 | Survival analysis. | Classes | 0.50 | computer room | |||||
| 8 | Sample size estimation (including clinical trials). Analysis of scientific publications. | Classes | 1.00 | computer room | |||||
| 9 | Students presentations. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the literature – prepare to lectures accordingly to the plan. 2. Individual analysis of scientific publication. 3. Individual work – every student will receive a research data file (or student can use their own) with previously defined research tasks. Student will statistically process data to reach defined tasks using descriptive statistic, inferential statistic and/or analytical statistics methods. As well as to report obtained results in final paper, using defined formatting style and to present obtained results in the last lecture. | ||||||||
| Assessment Criteria: | Participation in practical lectures. Scientific publication analysis, Recognise terminology used in statistics and basic methods used in different publications. To get a successful mark: 1. Multiple choice test about statistics – 50% 2. Scientific publication analysis – 30% 3. Individual work presentations – 20% | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | After successfully accomplished study course, students will have acquired knowledge to: Correctly interpret the main statistical tests; Describe measurement results using statistical parameters. | ||||||||
| Skills: | Will be able to define the hypotheses of basic statistical tests; Will be able to draw a normal distribution and calculate its main characteristic parameters; Will be able to calculate Pearson and Spearman correlation coefficients; Will be able to calculate and analyze the regression equation; Will be able to calculate independent and dependent sample t-tests; Will be able to perform Pearson's chi-square and Fisher's exact test; Will know how to use the processing program IBM SPSS for data processing and visualization; Will be able to assess the conformity of quantitative data with the existence of a normal distribution; Will be able to perform analysis of variance (ANOVA); Will be able to perform non-parametric tests - Mann-Whitney, Wilcoxon, Friedman and Kruskal-Wallis; Will know how to perform Kaplan-Meier survival analysis; Will be able to operate in the IBM SPSS computer program environment with data selection and perform the necessary calculations; Will be able to formulate the necessary statistical tests for analysis; analyze your own data; will be able to adequately process them and draw consequential and justified conclusions. | ||||||||
| Competencies: | As a result of learning the study course, students will be able to independently perform basic operations in the IBM SPSS environment, performing data processing, visualization and the necessary calculations. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Teibe U. Bioloģiskā statistika. Rīga: LU Akadēmiskais apgāds, 2007, p 155. (akceptējams izdevums) | ||||||||
| 2 | Field A. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| Additional Reading | |||||||||
| 1 | Altman D. Practical Statistics for Medical Research. Chapman & Hall, 1999, pp. 612. | ||||||||
