Modeling missing binary outcome data in a successful web-based smokeless tobacco cessation program
Article first published online: 9 FEB 2010
© 2010 The Authors. Journal compilation © 2010 Society for the Study of Addiction
Volume 105, Issue 6, pages 1005–1015, June 2010
How to Cite
Smolkowski, K., Danaher, B. G., Seeley, J. R., Kosty, D. B. and Severson, H. H. (2010), Modeling missing binary outcome data in a successful web-based smokeless tobacco cessation program. Addiction, 105: 1005–1015. doi: 10.1111/j.1360-0443.2009.02896.x
- Issue published online: 6 MAY 2010
- Article first published online: 9 FEB 2010
- Submitted 12 December 2008; initial review completed 2 March 2009; final version accepted 20 November 2009
- sensitivity analysis;
- smokeless tobacco;
- tobacco cessation;
- web-based intervention
Aim To examine various methods to impute missing binary outcome from a web-based tobacco cessation intervention.
Design The ChewFree randomized controlled trial used a two-arm design to compare tobacco abstinence at both the 3- and 6-month follow-up for participants randomized to either an enhanced web-based intervention condition or a basic information-only control condition.
Setting Internet in the United States and Canada.
Participants Secondary analyses focused upon 2523 participants in the ChewFree trial.
Measurements Point-prevalence tobacco abstinence measured at 3- and 6-month follow-up.
Findings The results of this study confirmed the findings for the original ChewFree trial and highlighted the use of different missing-data approaches to achieve intent-to-treat analyses when confronted with substantial attrition. The use of different imputation methods yielded results that differed in both the size of the estimated treatment effect and the standard errors.
Conclusions The choice of imputation model used to analyze missing binary outcome data can affect substantially the size and statistical significance of the treatment effect. Without additional information about the missing cases, they can overestimate the effect of treatment. Multiple imputation methods are recommended, especially those that permit a sensitivity analysis of their impact.