Does ignoring model selection when assessing the effect of particulate matter air pollution on mortality make us too vigilant?

Annals of Epidemiology
Steven Roberts, Michael A Martin

Abstract

To investigate the extent to which standard errors can be underestimated in time-series studies of the association between particulate matter air pollution (PM) and mortality if model selection variation is not accounted for. Actual-time series data from Cook County, Illinois, and Salt Lake County, Utah, for the period 1987 to 2000 were used to generate mortality time series. These series were used to examine the overconfidence resulting from ignoring variability introduced by the model selection process. When variation associated with a model selection process is not accounted for, we found that the estimated standard errors for the effect of PM on mortality were substantially smaller than the true standard errors that necessarily incorporate model selection variability. Because of this, the true standard errors are approximately 70% larger than the reported standard errors. We also found that not accounting for model selection effects can result in the observed size of tests of no association between PM and mortality being up to about five times the nominal significance level. Failing to account properly for the effect of model selection can reduce the accepted burden of proof for concluding a statistically significant associ...Continue Reading

References

Jan 9, 2004·Epidemiology·Michael J DanielsScott Zeger
Mar 5, 2005·American Journal of Epidemiology·Roger D PengJonathan M Samet
Aug 18, 2005·Journal of Exposure Science & Environmental Epidemiology·Michael A Martin, Steven Roberts
Apr 18, 2008·American Journal of Epidemiology·Michael A Martin, Steven Roberts
Nov 8, 2008·Environmental Health : a Global Access Science Source·Cizao Ren, Shilu Tong

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Citations

Jan 1, 2016·Structural Equation Modeling : a Multidisciplinary Journal·Gitta H Lubke, Ian Campbell

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