If you are experiencing problems downloading PDF or HTML fulltext, our helpdesk recommend clearing your browser cache and trying again. If you need help in clearing your cache, please click here . Still need help? Email help@ingentaconnect.com

Frailty Models with Missing Covariates

$48.00 plus tax (Refund Policy)

Download / Buy Article:

Abstract:

Summary.

We present a method for estimating the parameters in random effects models for survival data when covariates are subject to missingness. Our method is more general than the usual frailty model as it accommodates a wide range of distributions for the random effects, which are included as an offset in the linear predictor in a manner analogous to that used in generalized linear mixed models. We propose using a Monte Carlo EM algorithm along with the Gibbs sampler to obtain parameter estimates. This method is useful in reducing the bias that may be incurred using complete-case methods in this setting. The methodology is applied to data from Eastern Cooperative Oncology Group melanoma clinical trials in which observations were believed to be clustered and several tumor characteristics were not always observed.

Keywords: Frailty model; Gibbs sampling; Missing covariates; Monte Carlo EM algorithm; Random effects; Survival analysis

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.0006-341X.2002.00098.x

Affiliations: Department of Biostatistics, University of North Carolina, 3104-D McGavran-Greenberg Hall, Campus Box 7420, Chapel Hill, North Carolina 27599, U.S.A.

Publication date: March 1, 2002

Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more