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Inverse probability weighted Cox regression for doubly truncated data.

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.

SUBMITTER: Mandel M

PROVIDER: S-EPMC5843502 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Inverse probability weighted Cox regression for doubly truncated data.

Mandel Micha M de Uña-Álvarez Jacobo J Simon David K DK Betensky Rebecca A RA

Biometrics 20170908 2

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 cov ...[more]

PMID: 28886206

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