ESTIMATING INTERVENTION EFFECTS IN LONGITUDINAL-STUDIES
ESTIMATING INTERVENTION EFFECTS IN LONGITUDINAL-STUDIES
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DOI:
10.1093/oxfordjournals.aje.a115399
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发表时间:
1989-10-01
影响因子:
5
通讯作者:
JOHNSON, CA
中科院分区:
文献类型:
--
作者:
DWYER, JH;MACKINNON, DP;JOHNSON, CA
Longitudinal studies aimed at assessing the impact of interventions on disease risk factors often confront several statistical problems. These problems include 1) dependent variables measured by ordered categories, 2) numerous potentially relevant patterns of transition between outcome levels, 3) mixed units of analysis (e.g., assignment of social unit while theorizing in terms of individuals), 4) incomplete randomization, and 5) correlated estimates of successive occasions of longitudinal measurement. Longitudinal data on use of cigarettes, alcohol, and marijuana among adolescents (n = 1,244, complete data) from the Midwestern Prevention Project are used to demonstrate solutions to each of these problems: 1) a proportional odds regression model, 2) conditional logistic models of transitions with interactions between baseline levels and intervention effect, 3) a logistic model estimated with linear regression methods on measures aggregated by social unit, 4) conditional and unconditional models of effect magnitude, and 5) a repeated measures logistic regression technique. Panel data fit to the various models yielded the following conclusions concerning intervention effects in the Midwestern Prevention Project: reduction in the prevalence of cigarette users in treatment schools compared with control schools (8% vs. 18% smoked in the last week at one year follow-up), mixed evidence of an effect on marijuana use, and no evidence of an effect on alcohol use.