Cox model with interval-censored covariate in cohort studies

Soohyun Ahn, Johan Lim, Myunghee Cho Paik, Ralph L Sacco, Mitchell S. Elkind

Research output: Contribution to journalArticle

1 Scopus citations


In cohort studies the outcome is often time to a particular event, and subjects are followed at regular intervals. Periodic visits may also monitor a secondary irreversible event influencing the event of primary interest, and a significant proportion of subjects develop the secondary event over the period of follow-up. The status of the secondary event serves as a time-varying covariate, but is recorded only at the times of the scheduled visits, generating incomplete time-varying covariates. While information on a typical time-varying covariate is missing for entire follow-up period except the visiting times, the status of the secondary event are unavailable only between visits where the status has changed, thus interval-censored. One may view interval-censored covariate of the secondary event status as missing time-varying covariates, yet missingness is partial since partial information is provided throughout the follow-up period. Current practice of using the latest observed status produces biased estimators, and the existing missing covariate techniques cannot accommodate the special feature of missingness due to interval censoring. To handle interval-censored covariates in the Cox proportional hazards model, we propose an available-data estimator, a doubly robust-type estimator as well as the maximum likelihood estimator via EM algorithm and present their asymptotic properties. We also present practical approaches that are valid. We demonstrate the proposed methods using our motivating example from the Northern Manhattan Study.

Original languageEnglish (US)
JournalBiometrical Journal
StateAccepted/In press - Jan 1 2018


  • Cohort study
  • Cox model
  • Interval-censored covariates
  • Missing time-varying covariate

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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