On two-stage estimation of structural instrumental variable models

Byeong Yeob Choi, Jason P. Fine, M. Alan Brookhart

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Two-stage least squares estimation is popular for structural equation models with unmeasured confounders. In such models, both the outcome and the exposure are assumed to follow linear models conditional on the measured confounders and instrumental variable, which is related to the outcome only via its relation with the exposure. We consider data where both the outcome and the exposure may be incompletely observed, with particular attention to the case where both are censored event times.A general class of two-stage minimum distance estimators is proposed that separately fits linear models for the outcome and exposure and then uses a minimum distance criterion based on the reduced-form model for the outcome to estimate the regression parameters of interest. An optimal minimum distance estimator is identified which may be superior to the usual two-stage least squares estimator with fully observed data. Simulation studies demonstrate that the proposed methods perform well with realistic sample sizes. Their practical utility is illustrated in a study of the comparative effectiveness of colon cancer treatments, where the effect of chemotherapy on censored survival times may be confounded with patient status.

Original languageEnglish (US)
Pages (from-to)881-899
Number of pages19
JournalBiometrika
Volume104
Issue number4
DOIs
StatePublished - Dec 1 2017

Keywords

  • Censored data
  • Endogeneity
  • Instrumental variable
  • Resampling
  • Unmeasured confounder.

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'On two-stage estimation of structural instrumental variable models'. Together they form a unique fingerprint.

Cite this