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Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data

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  • Shih, Joanna H.
  • Lu, Shou-En

Abstract

Multi-level clustered failure time data arise when the clustering of data occurs at more than one level. It is of interest to estimate the relative risks of covariates and clustering effect of failure times at each level. We consider a nested random effect proportional hazards model, where a subcluster-specific frailty operates multiplicatively on the conditional hazard model, and its distribution function depends on a cluster-specific random frailty. Under this model, we propose a Monte-Carlo EM-based semiparametric estimation procedure to estimate regression coefficients, nonparametric baseline cumulative hazard and the association parameters. In addition, we derive a covariance matrix of the parameter estimates. We illustrate the proposed method using clustered survival data collected from a vitamin A supplementation trial in Nepal, where it is of scientific interest to assess the clustering effect of mortality within households and within villages. We use simulations to study the performance of the proposed estimation procedure.

Suggested Citation

  • Shih, Joanna H. & Lu, Shou-En, 2009. "Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3864-3871, September.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:11:p:3864-3871
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    References listed on IDEAS

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    1. Samuel Manda & Renate Meyer, 2005. "Age at first marriage in Malawi: a Bayesian multilevel analysis using a discrete time‐to‐event model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 439-455, March.
    2. Renjun Ma, 2003. "Random effects Cox models: A Poisson modelling approach," Biometrika, Biometrika Trust, vol. 90(1), pages 157-169, March.
    3. Joe, H., 1993. "Parametric Families of Multivariate Distributions with Given Margins," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 262-282, August.
    4. Joanna H. Shih & Shou-En Lu, 2007. "Analysis of Failure Time Data with Multilevel Clustering, with Application to the Child Vitamin A Intervention Trial in Nepal," Biometrics, The International Biometric Society, vol. 63(3), pages 673-680, September.
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    Cited by:

    1. Lee, Woojoo & Shi, Jian Qing & Lee, Youngjo, 2010. "Approximate conditional inference in mixed-effects models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 173-184, January.

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