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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Reducing and meta-analysing estimates from distributed lag non-linear models.

Antonio Gasparrini1, Ben Armstrong

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK. antonio.gasparrini@lshtm.co.uk

BMC Medical Research Methodology
|January 10, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to simplify complex distributed lag non-linear models (DLNMs) for environmental epidemiology. This allows for better meta-analysis of non-linear and delayed health effects, like temperature-mortality associations.

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Two-stage time series design is crucial in environmental epidemiology.
  • Distributed lag non-linear models (DLNMs) analyze non-linear and lagged relationships.
  • Multivariate meta-analysis pools multi-parameter association estimates.

Purpose of the Study:

  • To propose a method for synthesizing DLNMs into simpler, meta-analysis-compatible summaries.
  • To enable the application of DLNMs in two-stage analyses.
  • To facilitate the meta-analysis of complex non-linear and delayed associations.

Main Methods:

  • Developed a methodology to synthesize DLNMs into reduced parameter sets.
  • Utilized one-dimensional functions compatible with multivariate meta-analysis.
  • Implemented the framework in R using 'dlnm' and 'mvmeta' packages.

Main Results:

  • Successfully applied the method to a two-stage time series analysis of temperature-mortality associations.
  • Analyzed data from 10 regions in England and Wales.
  • Provided R code and data as supplementary material.

Conclusions:

  • The proposed methodology extends DLNM application in two-stage analyses.
  • Enables meta-analytical estimates of interpretable summaries from complex associations.
  • Relaxes assumptions and avoids simplifications of previous modeling approaches.