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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    • "Data were analyzed using Generalized Linear Mixed Models (GLMM). GLMM includes all participants, regardless of missing data and is the recommended intent-to-treat approach for analyzing smoking cessation trials (Hall et al., 2001). We began with the growth curve model described in the main outcome paper (Smits et al., in press). "
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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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