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Statistical analysis of daily smoking status in smoking cessation clinical trials
Author(s) -
Li Yimei,
Wileyto E. Paul,
Heitjan Daniel F.
Publication year - 2011
Publication title -
addiction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.424
H-Index - 193
eISSN - 1360-0443
pISSN - 0965-2140
DOI - 10.1111/j.1360-0443.2011.03519.x
Subject(s) - gee , generalized estimating equation , covariate , smoking cessation , abstinence , estimating equations , medicine , confidence interval , odds , demography , odds ratio , longitudinal study , logistic regression , repeated measures design , mixed model , statistics , mathematics , psychiatry , maximum likelihood , pathology , sociology
Aims Smoking cessation trials generally record information on daily smoking behavior, but base analyses on measures of smoking status at the end of treatment (EOT). We present an alternative approach that analyzes the entire sequence of daily smoking status observations. Methods We analyzed daily abstinence data from a smoking cessation trial, using two longitudinal logistic regression methods: a mixed‐effects (ME) model and a generalized estimating equations (GEE) model. We compared results to a standard analysis that takes abstinence status at EOT as outcome. We evaluated time‐varying covariates (smoking history and time‐varying drug effect) in the longitudinal analysis and compared ME and GEE approaches. Results We observed some differences in the estimated treatment effect odds ratios across models, with narrower confidence intervals under the longitudinal models. GEE yields similar results to ME when only baseline factors appear in the model, but gives biased results when one includes time‐varying covariates. The longitudinal models indicate that the quit probability declines and the drug effect varies over time. Both the previous day's smoking status and recent smoking history predict quit probability, independently of the drug effect. Conclusion When analysing outcomes of studies from smoking cessation interventions, longitudinal models with multiple outcome data points, rather than just end of treatment, can makes efficient use of the data and incorporate time‐varying covariates. The generalized estimating equations approach should be avoided when using time‐varying predictors.