Identification of multivariate responders and non-responders by using Bayesian growth curve latent class models
We propose a multivariate growth curve mixture model that groups subjects on the basis of multiple symptoms measured repeatedly over time. Our model synthesizes features of two models. First, we follow Roy and Lin in relating the multiple symptoms at each time point to a single latent variable. Second, we use the growth mixture model of Muthén and Shedden to group subjects on the basis of distinctive longitudinal profiles of this latent variable. The mean growth curve for the latent variable in each class defines that class's features. For example, a class of 'responders' would have a decline in the latent symptom summary variable over time. A Bayesian approach to estimation is employed where the methods of Elliott and co-workers are extended to estimate simultaneously the posterior distributions of the parameters from the latent variable and growth curve mixture portions of the model. We apply our model to data from a randomized clinical trial evaluating the efficacy of "bacillus" Calmette-Guerin in treating symptoms of interstitial cystitis. In contrast with conventional approaches using a single subjective global response assessment, we use the multivariate symptom data to identify a class of subjects where treatment demonstrates effectiveness. Simulations are used to confirm identifiability results and to evaluate the performance of our algorithm. Copyright (c) 2009 Royal Statistical Society.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 58 (2009)
Issue (Month): 4 ()
|Contact details of provider:|| Postal: 12 Errol Street, London EC1Y 8LX, United Kingdom|
Web page: http://wileyonlinelibrary.com/journal/rssc
More information through EDIRC
|Order Information:||Web: http://ordering.onlinelibrary.wiley.com/subs.asp?ref=1467-9876&doi=10.1111/(ISSN)1467-9876|
When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:58:y:2009:i:4:p:505-524. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.