Abstract

Asthma is a variable condition with an apparent tendency for a natural decline in asthma symptoms and health care use occurring as children age. As a result, asthma interventions using a pre-post design may overestimate the intervention effect when no proper control group is available. Investigate patterns of natural decline over time with increasing age in asthma symptoms and health care use of children. Develop a statistical procedure that enables adjustment that accounts for expected declines in these outcomes and is useable when intervention evaluations must rely solely on pre-post data. Mixed-effects models with mixture distributions were used to describe the pattern of symptoms and health care use in 3,021 children aged 2 to 15 years in a combined sample from three controlled trials. An adaptive least squares estimation was used to account for overestimation of intervention effects and make adjustments for pre-post only data. Termed "Adjustment for Natural Declines in Asthma Outcomes (ANDAO)," the adjustment method uses bootstrap sampling to create control cohorts comparable to subjects in the intervention study from existing control subjects. ANDAO accounts for expected declines in outcomes and is beneficial when intervention evaluations must rely solely on pre-post data. Children under 10 years of age experienced 18% (95% confidence interval, 15-21%) fewer symptom days and 28% (95% confidence interval, 24-32%) fewer symptom nights with each additional year of age. The decline was less than 10% after age 10 years, depending on baseline asthma severity. Emergency department visits declined regardless of baseline symptom frequency (P = 0.02). The adjustment method corrected estimates to within 2.4% of true effects through simulations using control cohorts. Because of the declines in symptoms and health care use expected with increasing age of children with asthma, pre-post comparisons will greatly overestimate intervention effects. The ANDAO provides means to adequately estimate treatment effects when a control group design is not possible.

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