Reducing and meta-analysing estimates from distributed lag non-linear models

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Abstract

The methodology proposed here extends the use of DLNMs in two-stage analyses, obtaining meta-analytical estimates of easily interpretable summaries from complex non-linear and delayed associations. The approach relaxes the assumptions and avoids simplifications required by simpler modelling approaches.

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Authors

Antonio Gasparrini

Royal Society of Tropical Medicine and Hygiene

Ben Armstrong

Boston University

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BMC Medical Research Methodology

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London School of Hygiene & Tropical Medicine

University of London

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Gasparrini et al. (Wed,) studied this question.

synapsesocial.com/papers/69d8bdbf8c03fbaff8befeef — DOI: https://doi.org/10.1186/1471-2288-13-1

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Rejoinder to commentaries on ‘Multivariate meta‐analysis: Potential and promise’· 2011 · 161 citations
Linear Mixed Models for Longitudinal Data· 2000 · 2,667 citations
Multivariate meta‐analysis· 2003 · 155 citations
Linear Mixed Models for Longitudinal Data· 1997 · 3,533 citations
Distributed Lag Linear and Non-Linear Models inR: The Packagedlnm· 2011 · 1,379 citations

Reducing and meta-analysing estimates from distributed lag non-linear models

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Abstract

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References and Citations

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Discussion

Cite this study

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