Publication related to RSI or an RSI staff member

The effect of concurvity in generalized additive models linking mortality to ambient particulate matter.

In recent years, a number of studies have applied generalized additive models to time series data to estimate associations between exposure to air pollution and cardiorespiratory morbidity and mortality. If concurvity, the nonparametric analogue of multicollinearity, is present in the data, statistical software such as S-plus can seriously underestimate the variance of fitted model parameters, leading to significance tests with inflated type 1 error. This paper uses computer simulation and analyses of actual epidemiologic data to explore this underestimation of standard errors. We provide a method for assessing concurvity in data and an alternate class of models that is unaffected by concurvity. We argue that some degree of concurvity is likely to be present in all epidemiologic time series datasets and we explore through the use of meta-analysis the possible impact of concurvity on the existing body of work relating ambient levels of sulfate particles to mortality.

Authors

  • Ramsay, Timothy O, Ramsay TO, Health Canada, Ottawa, Canada. tramsay@uottawa.ca

  • Burnett, Richard T, Burnett RT,

  • Krewski, Daniel, Krewski D,

YEAR OF PUBLICATION: 2003
SOURCE: Epidemiology. 2003 Jan;14(1):18-23. doi: 10.1097/00001648-200301000-00009.
JOURNAL TITLE ABBREVIATION: Epidemiology
JOURNAL TITLE: Epidemiology (Cambridge, Mass.)
ISSN: 1044-3983 (Print) 1044-3983 (Linking)
VOLUME: 14
ISSUE: 1
PAGES: 18-23
PLACE OF PUBLICATION: United States
ABSTRACT:
In recent years, a number of studies have applied generalized additive models to time series data to estimate associations between exposure to air pollution and cardiorespiratory morbidity and mortality. If concurvity, the nonparametric analogue of multicollinearity, is present in the data, statistical software such as S-plus can seriously underestimate the variance of fitted model parameters, leading to significance tests with inflated type 1 error. This paper uses computer simulation and analyses of actual epidemiologic data to explore this underestimation of standard errors. We provide a method for assessing concurvity in data and an alternate class of models that is unaffected by concurvity. We argue that some degree of concurvity is likely to be present in all epidemiologic time series datasets and we explore through the use of meta-analysis the possible impact of concurvity on the existing body of work relating ambient levels of sulfate particles to mortality.
LANGUAGE: eng
DATE OF PUBLICATION: 2003 Jan
DATE COMPLETED: 20030402
DATE REVISED: 20190916
MESH DATE: 2003/04/04 05:00
EDAT: 2002/12/25 04:00
STATUS: MEDLINE
PUBLICATION STATUS: ppublish
COMMENT IN:
OWNER: NLM

Related RSI Experts

Daniel Krewski

Chief Risk Scientist

Dr. Daniel Krewski is Chief Risk Scientist and co-founder of Risk Sciences International (RSI), a firm established in 2006 to bring evidence-based, multidisciplinary expertise to the challenge of understanding, managing, and communicating risk. As RSI’s inaugural CEO and long-time scientific...
Read More about Daniel Krewski