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December 2012 Truth and memory: Linking instantaneous and retrospective self-reported cigarette consumption
Hao Wang, Saul Shiffman, Sandra D. Griffith, Daniel F. Heitjan
Ann. Appl. Stat. 6(4): 1689-1706 (December 2012). DOI: 10.1214/12-AOAS557


Studies of smoking behavior commonly use the time-line follow-back (TLFB) method, or periodic retrospective recall, to gather data on daily cigarette consumption. TLFB is considered adequate for identifying periods of abstinence and lapse but not for measurement of daily cigarette consumption, thanks to substantial recall and digit preference biases. With the development of the hand-held electronic diary (ED), it has become possible to collect cigarette consumption data using ecological momentary assessment (EMA), or the instantaneous recording of each cigarette as it is smoked. EMA data, because they do not rely on retrospective recall, are thought to more accurately measure cigarette consumption. In this article we present an analysis of consumption data collected simultaneously by both methods from 236 active smokers in the pre-quit phase of a smoking cessation study. We define a statistical model that describes the genesis of the TLFB records as a two-stage process of mis-remembering and rounding, including fixed and random effects at each stage. We use Bayesian methods to estimate the model, and we evaluate its adequacy by studying histograms of imputed values of the latent remembered cigarette count. Our analysis suggests that both mis-remembering and heaping contribute substantially to the distortion of self-reported cigarette counts. Higher nicotine dependence, white ethnicity and male sex are associated with greater remembered smoking given the EMA count. The model is potentially useful in other applications where it is desirable to understand the process by which subjects remember and report true observations.


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Hao Wang. Saul Shiffman. Sandra D. Griffith. Daniel F. Heitjan. "Truth and memory: Linking instantaneous and retrospective self-reported cigarette consumption." Ann. Appl. Stat. 6 (4) 1689 - 1706, December 2012.


Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62129
MathSciNet: MR3058680
Digital Object Identifier: 10.1214/12-AOAS557

Keywords: Bayesian analysis , heaping , latent variables , longitudinal data , smoking cessation

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 4 • December 2012
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