Marginal analysis for clustered failure time data

Shou En Lu, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Clustered failure time data are commonly encountered in biomedical research where the study subjects from the same cluster (e.g., family) share the common genetic and/or environmental factors such that the failure times within the same cluster are correlated. Two approaches that are commonly used to account for the intra-cluster association are frailty models and marginal models. In this paper, we study the marginal proportional hazards model, where the structure of dependence between individuals within a cluster is unspecified. An estimation procedure is developed based on a pseudo-likelihood approach, and a risk set sampling method is proposed for the formulation of the pseudo-likelihood. The asymptotic properties of the proposed estimators are studied, and the related issues regarding the statistical efficiencies are discussed. The performances of the proposed estimator are demonstrated by the simulation studies. A data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS) is used to illustrate this methodology.

Original languageEnglish (US)
Pages (from-to)61-79
Number of pages19
JournalLifetime Data Analysis
Issue number1
StatePublished - Mar 2005


  • Clustered failure time data
  • Marginal proportional hazards model
  • Pseudo-likelihood function
  • Risk set sampling method

ASJC Scopus subject areas

  • Applied Mathematics


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