Schedule

SKI3011 Evidence Synthesis 2: Statistics in Systematic Reviewing

Note

Dates follow the UM academic calendar 2026-2027. Times and rooms are in the UM timetable.

The current week is highlighted. Click a week number for the week page.

Week	Date	Lecture	Tutorial	Assignments	Homework
1	Wed 31 Mar	Course overview Choosing your topic and research question Protocols on protocols.io Introduction: Covidence	Search and find your topic Import in Zotero and Covidence PRISMA flow chart in Covidence Choose your partner (groups of 1-3)		Create: protocols.io account and Covidence project Watch: Covidence webinar Covidence: title/abstract screening Install: R and RStudio
2	Wed 7 Apr	R basics: R as a calculator, vectors, data frames, importing data, scripts, basic plots	Covidence: full text screening and data extraction forms R: run the lecture script on your own	A1: Protocol registered on protocols.io (DL Tue 13 Apr, 11:59)	Qualitative data extraction: study characteristics and risk of bias (robvis) of all primary papers
3	Wed 14 Apr	Effect sizes with escalc(), rma(yi, vi), forest plots Proportions and correlations	Quiz 1: Interpreting R code and output from lecture 2 (R basics) Covidence: quantitative extraction, export to csv R: first meta-analysis on your own data		Quantitative data extraction of all primary papers
4	Wed 21 Apr	Binary (RR, OR) and continuous (MD, SMD) outcomes	Quiz 2: Interpreting R code and output from lecture 3 (meta-analysis in R I) R: meta-analysis on your own data with binary or continuous outcomes	A2: 1st draft: introduction, methods, results + R code + dataset (DL Fri 23 Apr, 23:59)
5	Wed 28 Apr	I², tau², prediction interval Funnel plot, Egger test, trim-and-fill Leave-one-out and cumulative meta-analysis	Quiz 3: Interpreting R code and output from lecture 4 (meta-analysis in R II) R: heterogeneity and publication bias on your own data	A3: 2nd draft + R code + dataset (DL Fri 30 Apr, 23:59)
6	Tue 4 May	Subgroup analysis and meta-regression	Quiz 4: Interpreting R code and output from lecture 5 (heterogeneity and publication bias) Peer review	A4: Individual peer review (DL Mon 10 May, 11:59)
7	Wed 12 May	Claude Code demo	Quiz 5: Interpreting R code and output from lecture 6 (subgroup analysis and meta-regression) AI on own project Course evaluation (oral)		Optional: install Claude Code
8	17-21 May	Exam week		A5: Final paper + R code + dataset (DL Fri 21 May, 23:59)

Assessment

Component	Weight	Notes
Quizzes 1-5 (paper, in the tutorial, interpreting R code and output)	25%	Best 4 of 5 count
A4 Individual peer review	15%	Individual
A5 Final paper + R code + dataset	60%	Group of 1-3 students
A1-A3 Progress assignments	0%	Pass/fail