Statistical Methods for Meta-Analysis
Overview
Summary of Course:
The course will cover general techniques used in meta-analysis. This includes summary statistics arising from a collection of independent studies and based upon effect measures such as risk or odds ratio, standardized difference or correlation coefficient.  Fixed effects and random effects approaches will be discussed. Special topics such as rare event meta-analysis and meta-regression will also be touched upon. Practical sessions (about 50% of the course time) will be used to illustrate the methodology at hand of case-studies.
Course objectives:
The course will provide an overview on the state-of-the-art methodology and aim to enable participants to undertake the statistical analysis of their own meta-analytic data independently.
Statistical package:
This course will use the package STATA throughout including some add-on packages such as METAN.

Location:
Kusadasi, Turkey

Speakers:
Professor Dankmar Böhning, Mr James Gallagher, Dr Kalliopi Mylona

Duration:
25th-26th  May 2015

 

SummerSchool in Cesme, Turkey, 25-26 May 2015

Meta-Analysis in Medical Research

Programme
Instructors: Professor Dankmar Böhning (DB), Dr Kalliopi Mylona (KM), and Mr James Gallagher (JG)
Organizers: Professor Mehmet Orman and Dr Timur Köse

Day 1

9.00    Opening
9:15    Lecture 1: Basic Elements (DB)
10:15 Coffee
10:45 Practical 0: Introduction into STATA (JG) and METAN (DB)
11:15 Practical 1: Basic Elements (DB)
12:30 Lunch
14:00 Lecture 2: Heterogeneity in Meta-Analysis (DB)
14:45 Practical 2: Heterogeneity in Meta-Analysis (DB)
15:15 Tea
15:45 Lecture 3: Cumulative Meta-Analysis (JG)
16:15 Practical 3: Cumulative Meta-Analysis (JG)
16:45 End of Day 1

Day 2

9:00  Lecture 4: Small study size effects (KM)
10:00 Practical 4: Small study size effects (KM)
10:45 Coffee
11:15 Lecture 5: Meta-Regression (DB)
12:00 Practical 5: Meta-Regression (DB)
12:30 Lunch
14:00 Lecture 6: Meta-Regression using generalized linear mixed models (DB)
15:15 Tea
15:45 Practical 6: Meta-Regression using generalized linear mixed models (DB)
16:00 Discussion and Case Studies
17:00 End of Day 2