Landmark Prediction of Long-Term Survival Incorporating Short-Term Event Time Information
AbstractIn recent years, a wide range of markers have become available as potential tools to predict risk or progression of disease. In addition to such biological and genetic markers, short-term outcome information may be useful in predicting long-term disease outcomes. When such information is available, it would be desirable to combine this along with predictive markers to improve the prediction of long-term survival. Most existing methods for incorporating censored short-term event information in predicting long-term survival focus on modeling the disease process and are derived under restrictive parametric models in a multistate survival setting. When such model assumptions fail to hold, the resulting prediction of long-term outcomes may be invalid or inaccurate. When there is only a single discrete baseline covariate, a fully nonparametric estimation procedure to incorporate short-term event time information has been previously proposed. However, such an approach is not feasible for settings with one or more continuous covariates due to the curse of dimensionality. In this article, we propose to incorporate short-term event time information along with multiple covariates collected up to a landmark point via a flexible varying-coefficient model. To evaluate and compare the prediction performance of the resulting landmark prediction rule, we use robust nonparametric procedures that do not require the correct specification of the proposed varying-coefficient model. Simulation studies suggest that the proposed procedures perform well in finite samples. We illustrate them here using a dataset of postdialysis patients with end-stage renal disease.
Download InfoIf 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.
Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of the American Statistical Association.
Volume (Year): 107 (2012)
Issue (Month): 500 (December)
Contact details of provider:
Web page: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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: (Michael McNulty).
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.