Extending distributed lag models to higher degrees

作者：Heaton Matthew J^*; Peng Roger D

来源：Biostatistics, 2014, 15(2): 398-412.

DOI：10.1093/biostatistics/kxt031

• Dimension reduction
• Gaussian process
• Heat exposure
• Lagged interaction
• NMMAPS dataset

Distributed lag (DL) models relate lagged covariates to a response and are a popular statistical model used in a wide variety of disciplines to analyze exposure-response data. However, classical DL models do not account for possible interactions between lagged predictors. In the presence of interactions between lagged covariates, the total effect of a change on the response is not merely a sum of lagged effects as is typically assumed. This article proposes a new class of models, called high-degree DL models, that extend basic DL models to incorporate hypothesized interactions between lagged predictors. The modeling strategy utilizes Gaussian processes to counterbalance predictor collinearity and as a dimension reduction tool. To choose the degree and maximum lags used within the models, a computationally manageable model comparison method is proposed based on maximum a posteriori estimators. The models and methods are illustrated via simulation and application to investigating the effect of heat exposure on mortality in Los Angeles and New York.

• 出版日期2014-4