Journal of Data Science

Abstract: Anti-smoking media campaign is an effective tobacco control strategy. How to identify what types of advertising messages are effective is important for maximizing the use of limited funding sources for such campaigns. In this paper, we propose a statistical modeling approach for systematically assessing the effectiveness of anti-smoking media campaigns based on ad recall rates and rating scores. This research is motivated by the need for evaluating youth responses to the Massachusetts Tobacco Control Program (MTCP) media campaign. Pattern-mixture GEE models are pro posed to evaluate the impact of viewer and ads characteristics on ad recall rates and rating scores controlling for missing values, confounding and cor relations in the data. A key difficulty for pattern-mixture modeling is that there were too many distinct missing data patterns which cause convergence problem for modeling fitting based on limited data. A heuristic argument based on collapsing missing data patterns is used to test the missing com pletely at random (MCAR) assumption in pattern-mixture GEE models. The proposed modeling approach and the recall-rating study design pro vide a complete system for identifying the most effective type of advertising messages.