Statistical Methods in Life Sciences
Description
This national course is open to PhD students, postdocs, and staff in the life sciences who need biostatistical skills in their research. Using real research examples, the course introduces core ideas of statistical inference and guides participants through widely used models such as regression, mixed-effects models for repeated or hierarchical data, and survival analysis. The course is suitable both for those with limited prior training and for researchers already applying statistical methods who want a stronger conceptual foundation to confidently choose, apply, and interpret statistical analyses in their own work.
Course content
- Probability, sampling, and distributions for biological data
- Sampling variability and resampling
- PCA and clustering
- Confidence intervals and hypothesis testing
- Linear models for continuous outcomes
- Generalized linear models for binary and count data
- Mixed-effects models for repeated or hierarchical data,
- Survival analysis and time-to-event data
A course fee of 3000 SEK for academic participants and 15000 SEK for non-academic participants will be invoiced to accepted participants. The fee includes lunches, coffee and snacks. Please note that NBIS cannot invoice individuals
Learning Outcomes
- Explain biological and technical sources of variability using probability concepts
- Quantify uncertainty using sampling, resampling, and appropriate distributions
- Identify structure in multivariate data using PCA and clustering
- Interpret confidence intervals and hypothesis tests correctly
- Build and interpret linear models for continuous biological outcomes
- Apply generalized linear models to binary and count data
- Analyze repeated or hierarchical data using mixed-effects models
- Analyze time-to-event data using survival analysis
Prerequisites & Technical Requirements
Prerequisites
Basic R programming skills. You can check your skills by taking our self-assessment test
Technical requirements
Applicants are expected to bring their own laptops. A reasonably modern laptop with linux/unix, mac or windows OS and internet connection. Latest version of R and R Studio should be installed.
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Affiliations & Networks
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