Error In Time
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article Open Access Open Peer Review This article has Open Peer Review reports available. How does Open Peer Review work? Measurement error in time-series analysis: a simulation study comparing modelled and monitored
Time Error Correction
dataBarbaraKButland1, BenArmstrong2Email author, RichardWAtkinson1, PaulWilkinson2, MathewRHeal3, RuthMDoherty4 and MassimoVieno5BMC Medical Research Methodology201313:136DOI: 10.1186/1471-2288-13-136©
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Butland et al.; licensee BioMed Central Ltd.2013Received: 9May2013Accepted: 4October2013Published: 13November2013 Open Peer Review reports Abstract Background Assessing health effects run time error from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and http://www.dailymotion.com/video/xnw6vf_the-error-in-time_shortfilms temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Methods Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-136 association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003–2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). Results When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked f
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