ArticleEnvironment & health (Washington, D.C.)2026
Three-Stage Method to Estimate Distributed Lag Effects for a Time-Series Analysis of Noncontinuous Exposure Data.
Article in Environment & health (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Distributed lag models (DLMs) are essential for estimating the delayed health effects of environmental exposures in a time-series analysis. However, their application is often limited by the requirement for complete continuous exposure data. Simpler lag-specific models with a single exposure can use more data but suffer from an omitted variable bias (OVB). This study develops and validates a three-stage method to accurately estimate distributed lag effects from noncontinuous exposure data by correcting for OVB. We propose a three-stage approach, fitting a series of lag-specific models using the noncontinuous data to obtain biased estimates in the first stage, correcting the bias by the autocorrelation structure of the exposure time series in the second stage, and smoothing the calibrated estimates in the third stage. The method's validity was confirmed by statistical simulations. We then applied it to a case study analyzing the association between PM
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