IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i1p113-127.html
   My bibliography  Save this article

Trends in smoking cessation: a Markov model approach

Author

Listed:
  • Charles G. Minard
  • Wenyaw Chan
  • David W. Wetter
  • Carol J. Etzel

Abstract

Intervention trials such as studies on smoking cessation may observe multiple, discrete outcomes over time. When the outcome is binary, participant observations may alternate between two states over the course of the study. The generalized estimating equation (GEE) approach is commonly used to analyze binary, longitudinal data in the context of independent variables. However, the sequence of observations may be assumed to follow a Markov chain with stationary transition probabilities when observations are made at fixed time points. Participants favoring the transition to one particular state over the other would be evidence of a trend in the observations. Using a log-transformed trend parameter, the determinants of a trend in a binary, longitudinal study may be evaluated by maximizing the likelihood function. A new methodology is presented here to test for the presence and determinants of a trend in binary, longitudinal observations. Empirical studies are evaluated and comparisons are made with the GEE approach. Practical application of the proposed method is made to the data available from an intervention study on smoking cessation.

Suggested Citation

  • Charles G. Minard & Wenyaw Chan & David W. Wetter & Carol J. Etzel, 2012. "Trends in smoking cessation: a Markov model approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 113-127, March.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:113-127
    DOI: 10.1080/02664763.2011.578619
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.578619
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.578619?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chris Corcoran & Louise Ryan & Pralay Senchaudhuri & Cyrus Mehta & Nitin Patel & Geert Molenberghs, 2001. "An Exact Trend Test for Correlated Binary Data," Biometrics, The International Biometric Society, vol. 57(3), pages 941-948, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Melody S. Goodman & Yi Li & Anne M. Stoddard & Glorian Sorensen, 2014. "Analysis of ordinal outcomes with longitudinal covariates subject to missingness," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1040-1052, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guogen Shan & Changxing Ma, 2014. "Efficient tests for one sample correlated binary data with applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 175-188, June.
    2. Bedrick, Edward J. & Hossain, Anwar, 2013. "Conditional tests for homogeneity of zero-inflated Poisson and Poisson-hurdle distributions," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 99-106.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:113-127. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.