Introduction to meta-analysis 2: Meta-analysis of binary and continuous outcomes

Topic category Statistical methods and meta-analysis
Date and Location
Date: 
Tuesday 2 October 2012 - 13:30 - 15:00
Location: 
Methods Group
Methods Group: 
Statistical Methods Group
Contact persons and facilitators
Contact person Affiliation Country
Joanne McKenzie (Contact this person) School of Public Health and Preventive Medicine, Monash University Australia
Facilitator Affiliation Country
Joanne McKenzie School of Public Health and Preventive Medicine, Monash University Australia
Malinee Laopaiboon Thai Cochrane Network, Department of Biostatistics, Faculty of Public Health, Khon Kaen University Thailand
Target audience
Target audience: 
Review authors
Is your workshop restricted to a specific audience or open to all Colloquium participants?: 
Open
Level of knowledge required: 
Intermediate
Type of workshop
Type of workshop: 
Training
Abstract text
Abstract: 
Objective:
The Cochrane Statistical Methods Group has developed a series of workshops addressing statistical guidelines as formulated in the Cochrane Handbook for Systematic Reviews of Interventions. This workshop will provide review authors with the knowledge of issues surrounding meta-analysis of binary and continuous outcomes.

Description:
Binary and continuous data are commonly encountered in health care. Pooling intervention effects from binary and continuous data presents unique methodological issues. Some of these issues will be discussed in this workshop. A brief introduction to meta-analysis of binary and continuous outcomes will be included, consisting of data extraction (extraction of event frquencies and/or effect estimates, and extraction of standard deviations from standard errors, confidence intervals, test statistics and P values); and dealing with outcomes measured on different scales. More complex issues will be discussed including options for pooling estimates of intervention effect when a mix of results from analyses using change from baseline and final values have been reported; and use of the generic inverse variance method. Issues will be illustrated by examples.