Student seminar series
Tianyi Pan
PhD candidate
Room: M3 3127
Generalized Distributed Lag Non-Linear Models using Penalized Splines for Estimating Associations Between Cumulative Exposure and Health
Quantifying associations between short-term exposure to ambient air pollution and health outcomes is an important public health priority. Historically, studies have restricted attention to single-day exposures or (equally weighted) average exposure over several days. Adaptive cumulative exposure distributed lag non-linear models (ACE-DLNMs), in contrast, quantify associations between health outcomes and cumulative exposure that is specified in a data-adaptive way. While the ACE-DLNM framework is highly interpretable, it is limited to continuous outcomes and does not scale well to large datasets. Motivated by a large analysis of daily pollution and circulatory and respiratory hospitalizations in Canada between 2001 and 2018, we propose a generalized ACE-DLNM incorporating penalized splines, and we propose an efficient estimation strategy based on profile likelihood and nested Laplace approximate marginal likelihood with Newton-type methods. Our proposed method improves upon existing approaches in three ways: (1) it applies to general response types, including over-dispersed counts; (2) estimation is computationally efficient and readily applies to large datasets; and (3) it treats the exposure process continuously with respect to time. In application to the motivating analysis, the proposed method respects the discrete responses and reduces uncertainty in estimated associations compared to generalized additive models with fixed exposures.