Imperial College London


Faculty of MedicineSchool of Public Health

Chair in Biostatistics



m.blangiardo Website




528Norfolk PlaceSt Mary's Campus






BibTex format

author = {Sera, F and Armstrong, B and Blangiardo, M and Gasparrini, A},
doi = {10.1002/sim.8362},
journal = {Statistics in Medicine},
pages = {5429--5444},
title = {An extended mixed-effects framework for meta-analysis},
url = {},
volume = {38},
year = {2019}

RIS format (EndNote, RefMan)

AB - Standard methods for metaanalysis are limited to pooling tasks in which a single effect size is estimated from a set of independent studies. However, this setting can be too restrictive for modern metaanalytical applications. In this contribution, we illustrate a general framework for metaanalysis based on linear mixedeffects models, where potentially complex patterns of effect sizes are modeled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of metaanalytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, doseresponse, and longitudinal metaanalysis and metaregression. The availability of a unified framework for metaanalysis, complemented with the implementation in a freely available and fully documented software, will provide researchers with a flexible tool for addressing nonstandard pooling problems.
AU - Sera,F
AU - Armstrong,B
AU - Blangiardo,M
AU - Gasparrini,A
DO - 10.1002/sim.8362
EP - 5444
PY - 2019///
SN - 0277-6715
SP - 5429
TI - An extended mixed-effects framework for meta-analysis
T2 - Statistics in Medicine
UR -
UR -
UR -
UR -
VL - 38
ER -