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A Cox-Aalen Model for Interval-censored Data

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  • Audrey Boruvka
  • Richard J. Cook

Abstract

type="main" xml:id="sjos12113-abs-0001"> The Cox-Aalen model, obtained by replacing the baseline hazard function in the well-known Cox model with a covariate-dependent Aalen model, allows for both fixed and dynamic covariate effects. In this paper, we examine maximum likelihood estimation for a Cox-Aalen model based on interval-censored failure times with fixed covariates. The resulting estimator globally converges to the truth slower than the parametric rate, but its finite-dimensional component is asymptotically efficient. Numerical studies show that estimation via a constrained Newton method performs well in terms of both finite sample properties and processing time for moderate-to-large samples with few covariates. We conclude with an application of the proposed methods to assess risk factors for disease progression in psoriatic arthritis.

Suggested Citation

  • Audrey Boruvka & Richard J. Cook, 2015. "A Cox-Aalen Model for Interval-censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 414-426, June.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:2:p:414-426
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    File URL: http://hdl.handle.net/10.1111/sjos.12113
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    References listed on IDEAS

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