Citation

Mandel M, de Uña-Álvarez J, Simon DK, Betensky RA. Biometrics 2018; 74(2): 481-487.

Affiliation

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, U.S.A.

Copyright

(Copyright © 2018, Biometric Society, Publisher John Wiley and Sons)

DOI

10.1111/biom.12771

PMID

28886206

Abstract

Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease.

© 2017, The International Biometric Society.

Language: en

Keywords

Biased data; Inverse weighting; Right truncation; U statistic