Literature DB >> 24432181

Truth and Memory: Linking Instantaneous and Retrospective Self-Reported Cigarette Consumption.

Hao Wang1, Saul Shiffman2, Sandra D Griffith3, Daniel F Heitjan3.   

Abstract

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.

Entities:  

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

Year:  2012        PMID: 24432181      PMCID: PMC3889075          DOI: 10.1214/12-AOAS557

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN: 1932-6157            Impact factor:   2.083


  11 in total

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Authors:  N R Boyd; R A Windsor; L L Perkins; J B Lowe
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Authors:  Yooseock Cheong; Hua-Hie Yong; Ron Borland
Journal:  Nicotine Tob Res       Date:  2007-08       Impact factor: 4.244

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Journal:  Biometrics       Date:  1991-12       Impact factor: 2.571

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Journal:  J Clin Epidemiol       Date:  1995-10       Impact factor: 6.437

7.  Tobacco harm reduction: conceptual structure and nomenclature for analysis and research.

Authors:  Saul Shiffman; Joe G Gitchell; Kenneth E Warner; John Slade; Jack E Henningfield; John M Pinney
Journal:  Nicotine Tob Res       Date:  2002       Impact factor: 4.244

8.  How many cigarettes did you smoke? Assessing cigarette consumption by global report, Time-Line Follow-Back, and ecological momentary assessment.

Authors:  Saul Shiffman
Journal:  Health Psychol       Date:  2009-09       Impact factor: 4.267

9.  Modeling heaping in self-reported cigarette counts.

Authors:  Hao Wang; Daniel F Heitjan
Journal:  Stat Med       Date:  2008-08-30       Impact factor: 2.373

10.  Quitting by gradual smoking reduction using nicotine gum: a randomized controlled trial.

Authors:  Saul Shiffman; Stuart G Ferguson; Kenneth R Strahs
Journal:  Am J Prev Med       Date:  2009-02       Impact factor: 5.043

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  7 in total

1.  Retail outlets prompt associative memories linked to the repeated use of nicotine and tobacco products among alternative high school students in California.

Authors:  James Russell Pike; Yusuke Shono; Nasya Tan; Bin Xie; Alan W Stacy
Journal:  Addict Behav       Date:  2019-07-30       Impact factor: 3.913

2.  Model-based imputation of latent cigarette counts using data from a calibration study.

Authors:  Sandra D Griffith; Saul Shiffman; Yimei Li; Daniel F Heitjan
Journal:  Int J Methods Psychiatr Res       Date:  2015-06-16       Impact factor: 4.035

3.  SEX, LIES AND SELF-REPORTED COUNTS: BAYESIAN MIXTURE MODELS FOR HEAPING IN LONGITUDINAL COUNT DATA VIA BIRTH-DEATH PROCESSES.

Authors:  Forrest W Crawford; Robert E Weiss; Marc A Suchard
Journal:  Ann Appl Stat       Date:  2015       Impact factor: 2.083

4.  The Effect of E-cigarette Commercials on Youth Smoking: A Prospective Study.

Authors:  James Russell Pike; Nasya Tan; Stephen Miller; Christopher Cappelli; Bin Xie; Alan W Stacy
Journal:  Am J Health Behav       Date:  2019-11-01

5.  A Robust Functional EM Algorithm for Incomplete Panel Count Data.

Authors:  Alexander Moreno; Zhenke Wu; Jamie Yap; Cho Lam; David W Wetter; Inbal Nahum-Shani; Walter Dempsey; James M Rehg
Journal:  Adv Neural Inf Process Syst       Date:  2020-12

6.  Biases in self-reported height and weight measurements and their effects on modeling health outcomes.

Authors:  Carmen D Ng
Journal:  SSM Popul Health       Date:  2019-05-10

7.  The Importance of Filter Collection for Accurate Measurement of Cigarette Smoking.

Authors:  Megan L Saddleson; E Paul Wileyto; Rahul Darwar; Susan Ware; Andrew A Strasser
Journal:  Tob Regul Sci       Date:  2017-07
  7 in total

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