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A Comparison of Methods for Analysing a Nested Frailty Model to Child Survival in Malawi

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Demographic and Health Surveys collect child survival times that are clustered at the family and community levels. It is assumed that each cluster has a specific, unobservable, random frailty that induces an association in the survival times within the cluster. The Cox proportional hazards model, with family and community random frailties acting multiplicatively on the hazard rate, is presented. The estimation of the fixed effect and the association parameters of the modified model is then examined using the Gibbs sampler and the expectation–maximization (EM) algorithm. The methods are compared using child survival data collected in the 1992 Demographic and Health Survey of Malawi. The two methods lead to very similar estimates of fixed effect parameters. However, the estimates of random effect variances from the EM algorithm are smaller than those of the Gibbs sampler. Both estimation methods reveal considerable family variation in the survival of children, and very little variability over the communities.
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Keywords: EM algorithm; Gibbs sampling; nested frailty; proportional hazards model

Document Type: Research Article

Affiliations: Dept of Statistics, University of Waikato, Private Bag 3105, Hamilton, New Zealand. Present address: Dept of Statistics, University of Auckland, New Zealand

Publication date: March 1, 2001

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