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Statistics and Computing

, Volume 27, Issue 2, pp 483–500 | Cite as

Rank-based estimation for semiparametric accelerated failure time model under length-biased sampling

  • Sy Han Chiou
  • Gongjun Xu
Article
  • 365 Downloads

Abstract

Length-biased sampling appears in many observational studies, including epidemiological studies, labor economics and cancer screening trials. To accommodate sampling bias, which can lead to substantial estimation bias if ignored, we propose a class of doubly-weighted rank-based estimating equations under the accelerated failure time model. The general weighting structures considered in our estimating equations allow great flexibility and include many existing methods as special cases. Different approaches for constructing estimating equations are investigated, and the estimators are shown to be consistent and asymptotically normal. Moreover, we propose efficient computational procedures to solve the estimating equations and to estimate the variances of the estimators. Simulation studies show that the proposed estimators outperform the existing estimators. Moreover, real data from a dementia study and a Spanish unemployment duration study are analyzed to illustrate the proposed method.

Keywords

Doubly-weighted estimating equation Induced smoothing  Resampling Length-biased sampling 

Notes

Acknowledgments

The authors are grateful to the editors and the reviewers for their helpful comments. The authors appreciate Professors Ian McDowell, Masoud Asgharian and Christina Wolfson for sharing the Canadian Study of Health and Aging data, and Professor Jacobo de Uña-Álvarez for providing the Spanish unemployment data set.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of BiotatisticsHarvard School of Public HealthBostonUSA
  2. 2.School of StatisticsUniversity of MinnesotaMinneapolisUSA

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