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Frailty Models with Missing Covariates

Citation

Herring, Amy H.; Ibrahim, Joseph G.; & Lipsitz, Stuart R. (2002). Frailty Models with Missing Covariates. Biometrics, 58(1), 98-109.

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.

URL

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

Reference Type

Journal Article

Journal Title

Biometrics

Author(s)

Herring, Amy H.
Ibrahim, Joseph G.
Lipsitz, Stuart R.

Year Published

2002

Volume Number

58

Issue Number

1

Pages

98-109

Reference ID

4318