Advanced Biostatistics with R WMCM-LE-ABwR-fak
Participants will learn how to perform and interpret ANOVA and Kruskal-Wallis tests, conduct power analysis, analyze qualitative data, and apply linear and logistic regression models. Additionally, the course covers survival analysis, equipping students with essential tools for time-to-event data analysis. Through hands-on exercises in R, students will enhance their ability to choose appropriate statistical methods and effectively communicate their findings.
Course outline:
1. Analysis of Quantitative Data II
• One-way ANOVA and Kruskal-Wallis test
• Assumptions and interpretation
2. Power Analysis
• Sample size determination
• Effect size and statistical power
3. Analysis of Qualitative Data
• Contingency tables, relative risk (RR), and odds ratio (OR)
• Diagnostic test performance: sensitivity, specificity, predictive values
• Choosing the appropriate statistical test for proportion comparisons
• Handling qualitative confounders
4. Linear Regression Analysis
• Model assumptions and interpretation
• Assessing model fit and performance
5. Logistic Regression Analysis
• Binary outcome modeling
• Odds ratios and predictive probabilities
6. Survival Analysis
• Kaplan-Meier estimation
• Log-rank test and Cox proportional hazards model
Grupa przedmiotów ogólnouczenianych
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Symbol/Symbole kierunkowe efektów uczenia się
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Wymagania wstępne
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Learning Outcomes
Upon completing the "Advanced Biostatistics with R" course, participants will:
• Acquire advanced skills in analyzing quantitative data through techniques such as ANOVA and the Kruskal-Wallis test, allowing them to assess differences among groups effectively.
• Gain proficiency in conducting power analysis to determine optimal sample sizes and understand the implications of statistical power in study design.
• Develop the ability to analyze qualitative data, utilizing contingency tables, relative risk, odds ratios, and diagnostic test performance metrics to draw meaningful conclusions from categorical data.
• Master linear regression analysis, including model interpretation and evaluation of fit, enabling them to examine relationships between variables.
• Learn to apply logistic regression analysis for modeling binary outcomes and interpreting results in the context of biomedical research.
• Understand survival analysis techniques, including Kaplan-Meier estimation and Cox proportional hazards models, for analyzing time-to-event data.
Kryteria oceniania
To receive a grade of 5 for the course, students must actively participate in classes, complete homework assignments, and successfully pass the final mini-project. If these conditions are not met, the final grade will be discussed on an individual basis.
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: