Statistical Analysis of Randomized Trials in Tobacco Treatment

Department of Psychiatry, University of California, San Francisco, 94143, USA.
Nicotine & Tobacco Research (Impact Factor: 3.3). 09/2001; 3(3):193-202. DOI: 10.1080/14622200110050411
Source: PubMed


This article considers two important issues in the statistical treatment of data from tobacco-treatment clinical trials: (1) data analysis strategies for longitudinal studies and (2) treatment of missing data. With respect to data analysis strategies, methods are classified as 'time-naïve' or longitudinal. Time-naïve methods include tests of proportions and logistic regression. Longitudinal methods include Generalized Estimating Equations and Generalized Linear Mixed Models. It is concluded that, despite some advantages accruing to 'time-naïve' methods, in most situations, longitudinal methods are preferable. Longitudinal methods allow direct effects of the tests of time and the interaction of treatment with time, and allow model estimates based on all available data. The discussion of missing data strategies examines problems accruing to complete-case analysis, last observation carried forward, mean substitution approaches, and coding participants with missing data as using tobacco. Distinctions between different cases of missing data are reviewed. It is concluded that optimal missing data analysis strategies include a careful description of reasons for data being missing, along with use of either pattern mixture or selection modeling. A standardized method for reporting missing data is proposed. Reference and software programs for both data analysis strategies and handling of missing data are presented.

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    • "After the treatment, there was face-to-face follow-up at 6 months. An intent-to-treat model was adopted when it was not possible to locate the participants (Hall et al., 2001). They were considered to be smokers at the same level (in terms of number of cigarettes and nicotine content) as in the pretreatment assessment. "
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    ABSTRACT: Introduction: Although quitting motivation predicts smoking cessation, there have been inconsistent findings regarding motivation predicting long-term maintenance of abstinence. Moreover, most such research has been conducted in North America and the United Kingdom. The aim of this study was to examine motivation to quit as a predictor of smoking cessation and of abstinence maintenance in a Spanish sample. Method: The sample comprised 286 Spanish smokers undergoing psychological treatment for smoking cessation. Motivation to quit was assessed pre-treatment and post-treatment with the Readiness to Quit Ladder. Abstinence post-treatment and at 6month follow-up was biochemically verified. Results: Participants with higher levels of pre-treatment and post-treatment motivation were more likely to be abstinent at the end of the treatment (OR=1.36) and at 6month follow-up (OR=4.88). Among abstainers at the end of the treatment (61.9%), higher levels of motivation to quit post-treatment predicted maintaining abstinence at 6months (OR=2.83). Furthermore, participants who failed to quit smoking reported higher levels of motivation to quit post-treatment than they had pretreatment (p<.001). Conclusions: Motivation to quit smoking predicted short and long-term cessation, and also predicted long-term maintenance of abstinence. These results have implications for understanding motivational processes of smoking cessation in general, while extending research to Spanish smokers. They may also help in the design of cessation and relapse-prevention interventions. Specifically, the results suggest that motivational enhancement is important throughout the cessation and maintenance periods.
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    • "For our primary hypothesis test, we assume participants lost to follow-up are smokers. Depending on actual follow-up rates achieved, the results may be presented using recommended new strategies [40] to deal with missing data. Characteristics associated with nonresponse in smokers are used with SOLAS and SAS programs for implementing multiple imputation procedures. "
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    • "To address these concerns, researchers have proposed to use generalized estimating equations (GEE) and generalized linear mixed-effects models (GLMM) [1]. Both GEE and GLMM use repeated outcome measures and take the intra-personal association into account, and provide a means to comparing intervention effects at each time point and to examining whether the effects vary over time [2]. GEE provides population-averaged estimates, and the covariate effects can be interpreted as in standard logistic regression models. "
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