title: Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.
abstract: An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors.
authors: Department of Hygiene, Epidemiology and Medical Statistics, Medical School, University of Athens, Athens, Greece.
authors: Konstantinos
authors: K
authors: Perrakis
authors: Alexandros
authors: A
authors: Gryparis
authors: Joel
authors: J
authors: Schwartz
authors: Alain
authors: A
authors: Le Tertre
authors: Klea
authors: K
authors: Katsouyanni
authors: Francesco
authors: F
authors: Forastiere
authors: Massimo
authors: M
authors: Stafoggia
authors: Evangelia
authors: E
authors: Samoli
mesh_terms: D000393:Air Pollutants / Q000009:adverse effects*; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D006115:Greece; D006801:Humans; D015233:Models, Statistical; D052638:Particulate Matter / Q000032:analysis; D012621:Seasons; D013997:Time Factors; D014505:Urban Population
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: air pollution; case-crossover design; misspecification bias; model selection; semi-parametric Poisson regression