Inverse probability weighting methods for Cox regression with right-truncated data

Bella Vakulenko-Lagun*, Micha Mandel, Rebecca A. Betensky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Right-truncated data arise when observations are ascertained retrospectively, and only subjects who experience the event of interest by the time of sampling are selected. Such a selection scheme, without adjustment, leads to biased estimation of covariate effects in the Cox proportional hazards model. The existing methods for fitting the Cox model to right-truncated data, which are based on the maximization of the likelihood or solving estimating equations with respect to both the baseline hazard function and the covariate effects, are numerically challenging. We consider two alternative simple methods based on inverse probability weighting (IPW) estimating equations, which allow consistent estimation of covariate effects under a positivity assumption and avoid estimation of baseline hazards. We discuss problems of identifiability and consistency that arise when positivity does not hold and show that although the partial tests for null effects based on these IPW methods can be used in some settings even in the absence of positivity, they are not valid in general. We propose adjusted estimating equations that incorporate the probability of observation when it is known from external sources, which results in consistent estimation. We compare the methods in simulations and apply them to the analyses of human immunodeficiency virus latency.

Original languageAmerican English
Pages (from-to)484-495
Number of pages12
Issue number2
StatePublished - 1 Jun 2020

Bibliographical note

Publisher Copyright:
© 2019 The International Biometric Society


  • positivity assumption
  • proportional hazards
  • retrospective ascertainment reverse time
  • selection bias
  • stabilized weights


Dive into the research topics of 'Inverse probability weighting methods for Cox regression with right-truncated data'. Together they form a unique fingerprint.

Cite this