Gasparrini, A. 2019. gasparrini/2019_sera_StatMed_Rcode. [Online]. Github. Available from: https://github.com/gasparrini/2019_sera_StatMed_Rcode
Gasparrini, A. gasparrini/2019_sera_StatMed_Rcode [Internet]. Github; 2019. Available from: https://github.com/gasparrini/2019_sera_StatMed_Rcode
Gasparrini, A (2019). gasparrini/2019_sera_StatMed_Rcode. [Data Collection]. Github. https://github.com/gasparrini/2019_sera_StatMed_Rcode
Alternative Title
Updated R code and data from Sera Statistics in Medicine 2019
Description
A general framework for meta-analysis based on linear mixed-effects models, where potentially complex patterns of effect sizes are modelled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta-analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose-response, and longitudinal meta-analysis and meta-regression. This extended meta-analytical framework is illustrated in: "An extended mixed-effects framework for meta-analysis".
Keywords
| Data capture method | Simulation |
|---|---|
| Date (Published in a 3rd party system) | 11 November 2019 |
| Language(s) of written materials | English |
| Data Creators | Gasparrini, A |
|---|---|
| LSHTM Faculty/Department | Faculty of Public Health and Policy > Dept of Public Health, Environments and Society |
| Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
| Date Deposited | 02 Jul 2021 14:07 |
|---|---|
| Last Modified | 08 Jul 2021 12:48 |
| Publisher | Github |
