This course will introduce students to methods of systematic reviews and meta-analysis: the statistical analysis used to synthesize summary data from multiple studies. Meta-analysis and systematic reviews are used in many disciplines, including social sciences (e.g. psychology, sociology and education), medical sciences and other disciplines. Meta-Analysis and systematic reviews are very important tools in evidence based policy making in numerous way, as they allow the results of existing studies to be combined fairly objectively, they establish general treatment effect (effect size) and they provide a degree to which effect size is robust and consistent across the kinds of studies sampled. Students will get hands-on experience in performing analyses using R. R is rapidly becoming the leading language in data science and statistics in every industry and field. R is an open source language with rapidly growing numbers of individual and institutional users. The introduction to this language will help students the understand basics of R: program interface, basic syntax, variables and basic operations, reading data into R, accessing R packages, how to handle data structures, and create own functions. In the final section, students will get acquainted with community developed packages providing common statistical analyses and they will practice conducting meta-analysis using R. The course is active learning oriented, with students presenting and preforming tasks in the class.

As it is a very demanding 300 level course, students are expected to take the responsibility for their learning and demonstrate a proactive attitude: come prepared to the class, complete homework/practice exercises and actively search for answers to their questions and unclarified issues.

Upon successfully completing this course, participants should be able to:

• Understand the benefits, limitations and controversies of systematic reviews and meta-analysis.

• Develop a question which can be answered using meta-analysis.

• Understand and describe the steps in conducting a systematic review.

• Describe the process used to collect and extract data from original reports.

• Describe methods to critically assess the risk of bias of clinical trials - Describe and interpret the results of meta-analyses.

• Create the data for a meta-analysis: (define inclusion and exclusion criteria, search for the evidence and extract data).

• Use a basic functionality of R: install R, create and import the data, use R as a calculator, write simple scripts, define and access data structures in R, load packages, use R for statistical analyses.

• Conduct a meta-analysis in R (compute an effect size, compute summary effects, assess heterogeneity of effects, test for differences in effect size across subgroups, asses bias and visualize results).

As it is a very demanding 300 level course, students are expected to take the responsibility for their learning and demonstrate a proactive attitude: come prepared to the class, complete homework/practice exercises and actively search for answers to their questions and unclarified issues.

Upon successfully completing this course, participants should be able to:

• Understand the benefits, limitations and controversies of systematic reviews and meta-analysis.

• Develop a question which can be answered using meta-analysis.

• Understand and describe the steps in conducting a systematic review.

• Describe the process used to collect and extract data from original reports.

• Describe methods to critically assess the risk of bias of clinical trials - Describe and interpret the results of meta-analyses.

• Create the data for a meta-analysis: (define inclusion and exclusion criteria, search for the evidence and extract data).

• Use a basic functionality of R: install R, create and import the data, use R as a calculator, write simple scripts, define and access data structures in R, load packages, use R for statistical analyses.

• Conduct a meta-analysis in R (compute an effect size, compute summary effects, assess heterogeneity of effects, test for differences in effect size across subgroups, asses bias and visualize results).

- Teacher: Marcin Sklad