Statistical Modelling 5 (2005), 309–325
Mining epidemiological time series: an approach
based on dynamic regression
Monica Chiogna
Dipartimento di Scienze Statistiche,
Università di Padova,
Via Cesare Battisti 241,
I–35121 Padova,
Italy
eMail: monica@stat.unipd.it
Carlo Gaetan
Dipartimento di Statistica,
Università Ca' Foscari,
Venezia,
Italy
Abstract:
In epidemiology, time-series regression models are
specially suitable for evaluating short-term
effects of time-varying exposures to pollution. To
summarize findings from different studies on different
cities, the techniques of designed meta-analyses have
been employed. In this context, city-specific findings
are summarized by an ‘effect size’ measured on a common
scale. Such effects are then pooled together on a
second hierarchy of analysis. The objective of this
article is to exploit exploratory analysis of city-specific
time series. In fact, when dealing with many sources
of data, that is, many cities, an exploratory analysis
becomes almost unaffordable. Our idea is to explore the
time series by fitting complete dynamic regression
models. These models are easier to fit than models
usually employed and allowimplementation of very fast
automated model selection algorithms. The idea is to
highlight the common features across cities through
this analysis,which might then be used to design the
meta-analysis. The proposal is illustrated by analysing
data on the relationship between daily nonaccidental
deaths and air pollution in the 20 US largest cities.
Keywords:
transfer function model; model selection; air pollution; mortality
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