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Strategies to Handle Missing Data in Meta-Analysis
| Content Provider | Scilit |
|---|---|
| Author | Rochani, Haresh Keko, Mario |
| Copyright Year | 2021 |
| Description | Meta-analysis is one of the popular statistical analysis which combines the results of multiple scientific studies. Missing data are extremely common in meta-analysis even with very carefully planned studies. There are three major sources of missing data in meta-analysis: 3) missing studies (also known as publication bias) 4) missing outcomes and 5) missing predictors. In this chapter, we will discuss the strategies and approaches to deal with missing data in meta-analysis with major focus on missing outcomes and missing predictors. Real data examples will be provided to illustrate the application of missing data methods in meta-analysis. Book Name: Applied Meta-Analysis with R and Stata |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2018-0-95041-6&isbn=9780429061240&doi=10.1201/9780429061240-10&format=pdf |
| Ending Page | 266 |
| Page Count | 14 |
| Starting Page | 253 |
| DOI | 10.1201/9780429061240-10 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-03-19 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Applied Meta-Analysis with R and Stata Missing Data in Meta Analysis Missing Predictors Missing Outcomes |
| Content Type | Text |
| Resource Type | Chapter |
