Estimating Transitional Probabilities with Cross-Sectional Data to Assess Smoking Behavior Progression: A Validation AnalysisXinguang Chen1* and Feng Lin2
- *Corresponding Author:
- Xinguang Chen, MD, PhD
Professor, Pediatric Prevention Research Center/Department of Pediatrics
Wayne State University School of Medicine
4707 St. Antoine Street, Hutzel W534
Detroit, Michigan 48201, USA
Tel: (313) 745-0564
E-mail: [email protected]
Received date: April 12, 2012; Accepted date: August 29, 2012; Published date: September 03, 2012
Citation: Chen X, Lin F (2012) Estimating Transitional Probabilities with Cross-Sectional Data to Assess Smoking Behavior Progression: A Validation Analysis. J Biomet Biostat S1:004. doi:10.4172/2155-6180.S1-004
Copyright: © 2012 Chen X, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background and objective: New analytical tools are needed to advance tobacco research, tobacco control planning and tobacco use prevention practice. In this study, we validated a method to extract information from crosssectional survey for quantifying population dynamics of adolescent smoking behavior progression.
Methods: With a 3-stage 7-path model, probabilities of smoking behavior progression were estimated employing the Probabilistic Discrete Event System (PDES) method and the cross-sectional data from 1997-2006 National Survey on Drug Use and Health (NSDUH). Validity of the PDES method was assessed using data from the National Longitudinal Survey of Youth 1997 and trends in smoking transition covering the period during which funding for tobacco control was cut substantively in 2003 in the United States.
Results: Probabilities for all seven smoking progression paths were successfully estimated with the PDES method and the NSDUH data. The absolute difference in the estimated probabilities between the two approaches varied from 0.002 to 0.076 (p>0.05 for all) and were highly correlated with each other (R2=0.998, p<0.01). Changes in the estimated transitional probabilities across the 1997-2006 reflected the 2003 funding cut for tobacco control.
Conclusions: The PDES method has validity in quantifying population dynamics of smoking behavior progression with cross-sectional survey data. The estimated transitional probabilities add new evidence supporting more advanced tobacco research, tobacco control planning and tobacco use prevention practice. This method can be easily extended to study other health risk behaviors.