Estimation in the Cox survival regression model with covariate measurement error and a changepoint

Sarit Agami*, David M. Zucker, Donna Spiegelman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.

Original languageAmerican English
Pages (from-to)1139-1163
Number of pages25
JournalBiometrical Journal
Volume62
Issue number5
DOIs
StatePublished - 1 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Keywords

  • MPPLE
  • SIMEX
  • measurement error
  • regression calibration
  • threshold

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