title: E-Cigarettes and Smoking Cessation: Insights and Cautions From a Secondary Analysis of Data From a Study of Online Treatment-Seeking Smokers.
abstract: BACKGROUND
  Evidence from observational studies regarding the association between electronic cigarette (e-cigarette) use and cessation is mixed and difficult to interpret. Utilizing 2 analytic methods, this study illustrates challenges common in analyses of observational data, highlights measurement challenges, and reports associations between e-cigarette use and smoking cessation.
  
  
  METHODS
  Data were drawn from an ongoing web-based smoking cessation trial. The sample was comprised of 2,123 participants with complete 3-month follow-up data. Logistic regression models with and without entropy balancing to control for confounds were conducted to evaluate the association between e-cigarette use and 30-day cigarette smoking abstinence.
  
  
  RESULTS
  At follow-up, 31.7% of participants reported using e-cigarettes to quit in the past 3 months. E-cigarette users differed from nonusers on baseline characteristics including cigarettes per day, Fagerström score, quit attempt in the past year, and previous use of e-cigarettes to quit. At follow-up, e-cigarette users made more quit attempts and employed more cessation aids than smokers who did not use e-cigarettes to quit. E-cigarette use was negatively associated with abstinence after adjustment for baseline characteristics; however, the association was not significant after additional adjustment for use of other cessation aids at 3 months.
  
  
  CONCLUSIONS
  The magnitude and significance of the estimated association between e-cigarette use and cessation in this study were dependent upon the analytical approach. Observational studies should employ multiple analytic approaches to address threats to validity. Future research should employ better measures of patterns of and reasons for e-cigarette use, frequency of e-cigarette use, and concurrent use of cessation aids.
authors.affiliation: Schroeder Institute for Tobacco Research and Policy Studies, Legacy, Washington, DC; Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; jpearson@legacyforhealth.org.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Pearson
authors.affiliation: Schroeder Institute for Tobacco Research and Policy Studies, Legacy, Washington, DC; Department of Oncology, Georgetown University Medical Center/Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC;
authors.forename: Cassandra A
authors.initials: CA
authors.lastname: Stanton
authors.affiliation: Schroeder Institute for Tobacco Research and Policy Studies, Legacy, Washington, DC;
authors.forename: Sarah
authors.initials: S
authors.lastname: Cha
authors.affiliation: Schroeder Institute for Tobacco Research and Policy Studies, Legacy, Washington, DC; Department of Oncology, Georgetown University Medical Center/Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC;
authors.forename: Raymond S
authors.initials: RS
authors.lastname: Niaura
authors.affiliation: Department of Oncology, Georgetown University Medical Center/Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC; Department of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC.
authors.forename: George
authors.initials: G
authors.lastname: Luta
authors.affiliation: Schroeder Institute for Tobacco Research and Policy Studies, Legacy, Washington, DC; Department of Oncology, Georgetown University Medical Center/Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC;
authors.forename: Amanda L
authors.initials: AL
authors.lastname: Graham
mesh_terms: D000328:Adult; D066300:Electronic Nicotine Delivery Systems / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data; D012907:Smoking / Q000453:epidemiology; D016540:Smoking Cessation / Q000379:methods* / Q000706:statistics & numerical data; D000074606:Smoking Prevention; D014481:United States / Q000453:epidemiology