Effects of deceptive self-reports of quitting on the results of treatment trials for smoking: a quantitative assessment

Abstract

Problems with self-report measures for smoking motivate the use of biochemical tests in treatment trials for smoking. These biochemical tests, unfortunately, are not perfect. In this paper, we present an algebraic model of bias in treatment trials for smoking. Bias is expressed in terms of the deception rate among continued smokers in a control group, the relative deception rate among continued smokers in an experimental group, and the sensitivity and specificity of a biochemical test which may be used either to confirm self-reports of quitting or to replace self-report entirely. For given test specificity and sensitivity, the model defines deception rates for which different biochemical testing strategies are preferred. The model is presented in the context of current knowledge on the phenomenon of deception among adult smokers. The paper concludes that better judgements regarding the role of biochemical tests in treatment trials for smoking require more precise information regarding the magnitude and determinants of deception.