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Estimating intervention effects in longitudinal studies.

Authors
  • Dwyer, J H
  • MacKinnon, D P
  • Pentz, M A
  • Flay, B R
  • Hansen, W B
  • Wang, E Y
  • Johnson, C A
Type
Published Article
Journal
American Journal of Epidemiology
Publisher
Oxford University Press
Publication Date
Oct 01, 1989
Volume
130
Issue
4
Pages
781–795
Identifiers
PMID: 2773920
Source
Medline
License
Unknown

Abstract

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 by social unit while theorizing in terms of individuals), 4) incomplete randomization, and 5) correlated estimates for 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 level 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.

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