Course Title: Basic and Clinical Biostatistics Course Code: CLS 293 Program: Clinical Laboratory Sciences Department: Clinical Laboratory Sciences College: College of Applied Medical Sciences Institution: Taibah University Version: 1 Last Revision Date: N/A A. General information about the course: 1. Course Identification 1. Credit hours: (2 CH) 1 T + 1 P 2. Course type A. ?University ? College ? Department ?Track ?Others B. ? Required ?Elective 3. Level/year at which this course is offered: Level: 3 Year: 2 4. Course general Description: This course introduces the fundamental concepts and methods of statistics as applied to healthcare-related fields. The course aims to equip students with the necessary statistical tools to analyze and interpret data through series of lectures, practical exercises, and assignments. 5. Pre-requirements for this course (if any): None 6. Co-requisites for this course (if any): None 7. Course Main Objective(s): • To cover fundamentals of basic and clinical biostatistics. • To explain the use of descriptive data and testing associations. • To provide hands-on experience on how to obtain descriptive data and data obtained by testing associations. 2. Teaching mode (mark all that apply) No Mode of Instruction Contact Hours Percentage 1 Traditional classroom 45 100% 2 E-learning 0 0 3 Hybrid 0 0 No Mode of Instruction Contact Hours Percentage • Traditional classroom • E-learning 4 Distance learning 0 0 3. Contact Hours(based on the academic semester) No Activity Contact Hours 61. Lectures 15 62. Laboratory/Studio 30 63. Field 0 64. Tutorial 0 65. Others (specify) 0 Total 45 B. Course Learning Outcomes (CLOs), Teaching Strategies and Assessment Methods Code Course Learning Outcomes Code of PLOs aligned with program Teaching Strategies Assessment Methods 1.0 Knowledge and understanding 1.1 Explain basic and clinical biostatistics. K1 • Lectures • Training • Problem-based learning • Multiple Choice Questions (MCQs) • Short and long essay questions • Lab reports 1.2 Discuss the use of each type of tests and hypothesis tested. K2 • Lectures • Training • Problem-based learning • Multiple Choice Questions (MCQs) • Short and long essay questions • Lab reports 1.3 Describe the process of statistical testing. K3 • Lectures • Training • Problem-based learning • Multiple Choice Questions (MCQs) • Short and long essay questions Code Course Learning Outcomes Code of PLOs aligned with program Teaching Strategies Assessment Methods • Lab reports 2.0 Skills 2.1 Write statistical hypothesis. S1 • Lectures • Training • Web-based learning • Problem-based learning • Short essay questions • Lab reports 2.2 Perform statistical tests to address research questions and interpret clinical research findings S2 • Lectures • Training • Web-based learning • Problem-based learning • Lab reports • Practical applications questions 3.0 Values, autonomy, and responsibility 3.1 Communicate findings obtained from statistical testing oral and in written format. V3 • Lectures • Training • Web-based learning • Problem-based learning • Lab reports • Practical applications questions C. Course Content No List of Topics Contact Hours 11. Course Overview and Introduction to the Course 1 12. Getting Data Ready for SPSS: Data cleaning, import data on SPSS, coding data, creating new variables 1 3. Assessing the Normality of a Continuous Data 1 4. Descriptive Statistics I: Measures of Central Tendency (mean, median, mode) 1 5. Descriptive Statistics II: Measures of Dispersion (variance, standard deviation) 1 6. Comparison Tests I: Comparing the Mean of Two Groups (parametric test) 1 7. Comparison Tests II: Comparing the Median of Two Groups (nonparametric test) 1 8. Comparison Tests III: Comparing the Mean of the Different Groups (parametric test) 1 9. Comparison Tests IV: Comparing the Median of the Different Groups (non-parametric test) 1 10. Association Tests I: Chi-square/Fisher’s Exact test 1 11. Association Tests II: Correlation (Pearson and Spearman) 1 12. Regression Analysis I: Linear Regression Analysis 1 13. Regression Analysis II: Logistic Regression Analysis 1 14. Survival Analysis 1 15. Writing and Communicating Results in Text, Tables, and Figures 1 Practical Sessions 1. Introduction to SPSS or GraphPad Prism 2 2. Hands-On Training: Data cleaning, import data, coding data, creating new variables 2 3. Hands-On Training: Assessing the Normality of a Continuous Data 2 4. Hands-On Training: Measures of Central Tendency (mean, median, mode) 2 5. Hands-On Training: Measures of Dispersion (variance, standard deviation) 2 6. Hands-On Training: Comparing the Mean of Two Groups (parametric test) 2 7. Hands-On Training: Comparing the Median of Two Groups (nonparametric test) 2 8. Hands-On Training: Comparing the Mean of the Different Groups (parametric test) 2 9. Hands-On Training: Comparing the Median of the Different Groups (non-parametric test) 2 10. Hands-On Training: Chi-square/Fisher’s Exact test 2 11. Hands-On Training: Correlation (Pearson and Spearman) 2 12. Hands-On Training: Linear Regression Analysis 2 13. Hands-On Training: Logistic Regression Analysis 2 14. Hands-On Training: Survival Analysis 2 15. Hands-On Training: Writing and Communicating Results in Text, Tables, and Figures 2 Total 45 D. Students Assessment Activities No Assessment Activities * Assessment timing (in week no) Percentage of Total Assessment Score 37. Continuous assessment: Quizzes Ongoing 5% 38. Lab reports Week 14 5% 39. Assignment Week 12 5% No Assessment Activities * Assessment timing (in week no) Percentage of Total Assessment Score 40. Written test: Midterm exam Week 8-9 15% 5. Written test: Practical exam Week 16 30% 6. Written test: Final theoretical exam Week 17-18 40% *Assessment Activities (i.e., Written test, oral test, oral presentation, group project, essay, etc.). E. Learning Resources and Facilities 1. References and Learning Resources Essential References • Rosner B. (2016). Fundamentals of Biostatistics, 8th ed. Cengage Learning. Supportive References Electronic Materials Other Learning Materials • Statistical Analysis Software (software) 2. Required Facilities and equipment Items Resources facilities (Classrooms, laboratories, exhibition rooms, simulation rooms, etc.) Classrooms, Computer Lab Technology equipment (projector, smart board, software) Projector, Statistical Analysis Software (SPSS) or GraphPad Prism Other equipment (depending on the nature of the specialty) F. Assessment of Course Quality Assessment Areas/Issues Assessor Assessment Methods Effectiveness of teaching Instructor ü Direct Method (Rubrics) ü Indirect Method (Questionnaire on the course by the end of the semester) Effectiveness of Students' assessment Head of the department Student achievement is verified by the head of the department. Quality of Learning resources Instructor Student ü Course Report. ü Course Evaluation Survey (CES) Assessment Areas/Issues Assessor Assessment Methods Achievement of Course Learning Outcomes Instructor Students ü Direct Method (Rubrics) ü Indirect Method (Questionnaire on the course by the end of the semester) ü Course Evaluation Survey (CES) Effectiveness of Assessment Results Instructor ü Course Report ü Grade Report Analysis ü Course Evaluation Survey (Indirect) Assessors(Students, Faculty, Program Leaders, Peer Reviewer, Others (specify) Assessment Methods(Direct, Indirect)