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Statistics and Computing

, Volume 24, Issue 4, pp 559–568 | Cite as

Fast accelerated failure time modeling for case-cohort data

  • Sy Han Chiou
  • Sangwook Kang
  • Jun Yan
Article

Abstract

Semiparametric accelerated failure time (AFT) models directly relate the expected failure times to covariates and are a useful alternative to models that work on the hazard function or the survival function. For case-cohort data, much less development has been done with AFT models. In addition to the missing covariates outside of the sub-cohort in controls, challenges from AFT model inferences with full cohort are retained. The regression parameter estimator is hard to compute because the most widely used rank-based estimating equations are not smooth. Further, its variance depends on the unspecified error distribution, and most methods rely on computationally intensive bootstrap to estimate it. We propose fast rank-based inference procedures for AFT models, applying recent methodological advances to the context of case-cohort data. Parameters are estimated with an induced smoothing approach that smooths the estimating functions and facilitates the numerical solution. Variance estimators are obtained through efficient resampling methods for nonsmooth estimating functions that avoids full blown bootstrap. Simulation studies suggest that the recommended procedure provides fast and valid inferences among several competing procedures. Application to a tumor study demonstrates the utility of the proposed method in routine data analysis.

Keywords

Induced smoothing Multiplier bootstrap Resampling 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of ConnecticutStorrs-MansfieldUSA
  2. 2.Institute for Public Health ResearchUniversity of Connecticut Health CenterEast HartfordUSA
  3. 3.Center for Environmental Sciences & EngineeringUniversity of ConnecticutStorrs-MansfieldUSA

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