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

Bibliographic Data

ID23316461
AuthorsAntonio Gasparrini (0000-0002-2271-3568, University of London, corresponding author), Ben Armstrong (0000-0002-3354-4376, London School of Hygiene & Tropical Medicine)
Year2013
Volume13
Issue1
Pages1-1
Publication date2013-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMC Medical Research Methodology (JOURNAL)
Journal identifiersISSN: 1471-2288 • E-ISSN: 1471-2288
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1186/1471-2288-13-1
PMID23297754
PMCIDPMC3599933
OpenAlexW2074344667
LanguageEN
Citations received150
References cited24

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.

Data mining · Distributed lag · Econometrics · Lag · Linear model · Machine learning · Multivariate analysis · Multivariate statistics · Series (stratigraphy) · Set (abstract data type) · Stage (stratigraphy) · Statistics · Air Quality and Health Impacts · Computer Science · COVID-19 epidemiological studies · Mathematics · Statistical Methods and Bayesian Inference

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