Inverse probability weighted Cox regression for doubly truncated data

Micha Mandel, Jacobo de Uña-Álvarez, David K. Simon, Rebecca A. Betensky

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)481-487
Number of pages7
Issue number2
StatePublished - Jun 2018


  • Biased data
  • Inverse weighting
  • Right truncation
  • U statistic

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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