Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models

Torben Martinussen*, Stijn Vansteelandt, Eric J. Tchetgen Tchetgen, David M. Zucker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.

Original languageAmerican English
Pages (from-to)1140-1149
Number of pages10
JournalBiometrics
Volume73
Issue number4
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017, The International Biometric Society

Keywords

  • Causal effect
  • Confounding
  • Current treatment interaction
  • G-estimation
  • Instrumental variable
  • Mendelian randomization

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