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Estimating Intervention Effects in a Complex Multi-Level Smoking Prevention Study

By Milena Falcaro, Andrew C. Povey, Anne Fielder, Elizabeth Nahit and Andrew Pickles


This paper illustrates how to estimate cumulative and non-cumulative treatment effects in a complex school-based smoking intervention study. The Instrumental Variable method is used to tackle non-compliance and measurement error for a range of treatment exposure measures (binary, ordinal and continuous) in the presence of clustering and dropout. The results are compared to more routine analyses. The empirical findings from this study provide little encouragement for believing that poorly resourced school-based interventions can bring about substantial long-lasting reductions in smoking behaviour but that novel components such as a computer game might have some short-term effect

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    1. (2005). A comparison of intent-to-treat and per-protocol results in antibiotic noninferiority trials.
    2. (2006). A meta-analysis of teen cigarette smoking cessation. Health Psychol.
    3. (2003). A review of 25 long-term adolescent tobacco and other drug use prevention program evaluations.
    4. (2005). A systematic review of school-based smoking prevention trials with long-term follow-up.
    5. (1999). Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika
    6. (1992). An overview of methods for the analysis of longitudinal data.
    7. (1991). Analysis of clinical trials by treatment actually received: is it really an option?
    8. (2003). Analysis of Longitudinal Data.
    9. (2000). Causality: models, reasoning and inference.
    10. (1997). Econometric Analysis. Prentice Hall: Upper Saddle River,
    11. (1996). Factors influencing agreement between self-reports and biological measures of smoking among adolescents.
    12. (1996). Handling missing data in survey research.
    13. (1994). Identification and estimation of local average treatment effects. Econometrica
    14. (2004). Instrumental variables technique: cigarette price provided better estimate of effects of smoking on SF-12.
    15. (1997). Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations.
    16. (1984). Instrumental variables.
    17. (1997). IV for logistic regression: an illustration.
    18. (1986). Longitudinal data analysis using generalized linear models. Biometrika
    19. (2003). Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?
    20. (1986). Model robust confidence intervals using maximum likelihood estimators.
    21. (1988). Models for longitudinal data: a generalized estimating equation approach. Biometrics
    22. (1997). Predictors of risk for different stages of adolescent smoking in a biracial sample.
    23. (1961). Principles of Medical Statistics. The Lancet Limited:
    24. (1982). Random effects models for longitudinal data: an overview of recent results. Biometrics
    25. (0012). School-based programmes for preventing smoking.
    26. (1996). sg61: Bivariate probit models.
    27. (2003). Stata Statistical Software: release 8.0. Stata press: Lakeway Drive College Station,
    28. (1967). The behaviour of maximum likelihood estimators under non-standard conditions.
    29. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika
    30. (2006). The European Smoking prevention Framework Approach (ESFA): effects after 24 and 30 months. Health Educ. Res.
    31. (2003). The risk of smoking in relation to engagement with a school-based smoking intervention.
    32. (1993). The role of sampling weights when modeling survey data.
    33. (1987). The validity of smoking self-reports by adolescents: a re-examination of the bogus pipeline procedure.
    34. (1998). Tutorial in Biostatistics: propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.
    35. (2004). Working Paper 160. U.C. Berkeley Division of Biostatistics Working Paper Series. Berkeley Electronic Press:
    36. (1993). Youth tobacco use: risks, patterns, and control. In: Nicotine addiction: principles and management.

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