2026-04-13T06:52:06Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/2842024-06-13T09:58:13Zcom_2072_453049com_2072_2054col_2072_453050

00925njm 22002777a 4500 dc Tobías, Aurelio author Sáez Zafra, Marc author 2004-03 Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants http://hdl.handle.net/10256/284 Sèries temporals -- Anàlisi Medi ambient -- Contaminació Time-series regression models to study the short-term effects of environmental factors on health