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BMC Med Res Methodol. 2013 Jan 9;13:1. doi: 10.1186/1471-2288-13-1.

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

Author information

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

Abstract

BACKGROUND:

The two-stage time series design represents a powerful analytical tool in environmental epidemiology. Recently, models for both stages have been extended with the development of distributed lag non-linear models (DLNMs), a methodology for investigating simultaneously non-linear and lagged relationships, and multivariate meta-analysis, a methodology to pool estimates of multi-parameter associations. However, the application of both methods in two-stage analyses is prevented by the high-dimensional definition of DLNMs.

METHODS:

In this contribution we propose a method to synthesize DLNMs to simpler summaries, expressed by a reduced set of parameters of one-dimensional functions, which are compatible with current multivariate meta-analytical techniques. The methodology and modelling framework are implemented in R through the packages dlnm and mvmeta.

RESULTS:

As an illustrative application, the method is adopted for the two-stage time series analysis of temperature-mortality associations using data from 10 regions in England and Wales. R code and data are available as supplementary online material.

DISCUSSION AND CONCLUSIONS:

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.

PMID:
23297754
PMCID:
PMC3599933
DOI:
10.1186/1471-2288-13-1
[Indexed for MEDLINE]
Free PMC Article
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