The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.

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The effect of daily ambient air pollution was examined within a cohort of 846 asthmatic children residing in eight urban areas of the USA, using data from the National Cooperative Inner-City Asthma Study.Daily air pollution concentrations were extracted from the Aerometric Information Retrieval System database from the Environment Protection Agency in the USA. Mixed linear models and generalized estimating equation models were used to evaluate the effects of several air pollutants (ozone, sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and particles with a 50% cut-off aerodynamic diameter of 10 mm (PM10) on peak expiratory flow rate (PEFR) and symptoms in 846 children with a history of asthma (ages 4-9 yrs).None of the pollutants were associated with evening PEFR or symptom reports. Only ozone was associated with declines in morning % PEFR (0.59% decline (95% confidence interval (CI) 0.13-1.05%) per interquartile range (IQR) increase in 5-day average ozone). In single pollutant models, each pollutant was associated with an increased incidence of morning symptoms: (odds ratio (OR)=1.16 (95% CI 1.02-1.30) per IQR increase in 4-day average ozone, OR=1.32 (95% CI 1.03-1.70) per IQR increase in 2-day average SO 2 , OR=1.48 (95% CI 1.02-2.16) per IQR increase in 6-day average NO 2 and OR=1.26 (95% CI 1.0-1.59) per IQR increase in 2-day average PM10.This longitudinal analysis supports previous time-series findings that at levels below current USA air-quality standards, summer-air pollution is significantly related to symptoms and decreased pulmonary function among children with asthma. Much of the evidence for the effect of air pollution on respiratory health [1-8] is based on time-series analyses of repeated measurements in closed cohorts, which create a daily summary of responses across all study individuals. Fluctuations in this summary measure are evaluated relative to daily fluctuations in air pollution. Therefore, these approaches are not well suited to investigations of individual-level factors related to heterogeneity of response. Time-series analyses require that the distribution of individuallevel factors in the study population remain stable over time [9] or that data on changes in these characteristics are included in the model. This limits their usefulness in studying populations which do not remain fixed during the study period.Longitudinal analysis techniques such as mixed linear models and generalized estimating equations provide a more statistically powerful alternative by incorporating individual level outcomes and covariates.They permit estimation of individual mean effects and individual change over time as well as population mean effects over the entire study period. These methods require no assumptions about stability of population characteristics over time and subjects with incomplete data can be included in the analysis [10]. Therefore these methods are well-suited for epidemiological studies.These methods were used to evaluate air pollutionrelated health effects in a large cohort of...

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Marginal structural models (MSMs) are being used more frequently to obtain causal effect estimates in observational studies. Although the principal estimator of MSM coefficients has been the inverse probability of treatment weight (IPTW) estimator, there are few published examples that illustrate how to apply IPTW or discuss the impact of model selection on effect estimates. The authors applied IPTW estimation of an MSM to observational data from the Fresno Asthmatic Children's Environment Study (2000-2002) to evaluate the effect of asthma rescue medication use on pulmonary function and compared their results with those obtained through traditional regression methods. Akaike's Information Criterion and cross-validation methods were used to fit the MSM. In this paper, the influence of model selection and evaluation of key assumptions such as the experimental treatment assignment assumption are discussed in detail. Traditional analyses suggested that medication use was not associated with an improvement in pulmonary function--a finding that is counterintuitive and probably due to confounding by symptoms and asthma severity. The final MSM estimated that medication use was causally related to a 7% improvement in pulmonary function. The authors present examples that should encourage investigators who use IPTW estimation to undertake and discuss the impact of model-fitting procedures to justify the choice of the final weights.

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BackgroundAlthough studies have demonstrated that air pollution is associated with exacerbation of asthma symptoms in children with asthma, little is known about the susceptibility of subgroups, particularly those with atopy.ObjectiveThis study was designed to evaluate our a priori hypothesis that identifiable subgroups of asthmatic children are more likely to wheeze with exposure to ambient air pollution.MethodsA cohort of 315 children with asthma, 6–11 years of age, was recruited for longitudinal follow-up in Fresno, California (USA). During the baseline visit, children were administered a respiratory symptom questionnaire and allergen skin-prick test. Three times a year, participants completed 14-day panels during which they answered symptom questions twice daily. Ambient air quality data from a central monitoring station were used to assign exposures to the following pollutants: particulate matter ≤ 2.5 μm in aerodynamic diameter, particulate matter between 2.5 and 10 μm in aerodynamic diameter (PM10–2.5), elemental carbon, nitrogen dioxide (NO2), nitrate, and O3.ResultsFor the group as a whole, wheeze was significantly associated with short-term exposures to NO2 [odds ratio (OR) = 1.10 for 8.7-ppb increase; 95% confidence interval (CI), 1.02–1.20] and PM10–2.5 (OR = 1.11 for 14.7-μg/m3 increase; 95% CI, 1.01–1.22). The association with wheeze was stronger for these two pollutants in children who were skin-test positive to cat or common fungi and in boys with mild intermittent asthma.ConclusionA pollutant associated with traffic emissions, NO2, and a pollutant with bioactive constituents, PM10–2.5, were associated with increased risk of wheeze in asthmatic children living in Fresno, California. Children with atopy to cat or common fungi and boys with mild intermittent asthma were the subgroups for which we observed the largest associations.

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