Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses

Tom M Palmer, John R Thompson, Martin D Tobin, Nuala A Sheehan, Paul R Burton

Research output: Contribution to journalArticle (Academic Journal)peer-review

54 Citations (Scopus)

Abstract

BACKGROUND: Mendelian randomization uses a carefully selected gene as an instrumental-variable (IV) to test or estimate an association between a phenotype and a disease. Classical IV analysis assumes linear relationships between the variables, but disease status is often binary and modelled by a logistic regression. When the linearity assumption between the variables does not hold the IV estimates will be biased. The extent of this bias in the phenotype-disease log odds ratio of a Mendelian randomization study is investigated.

METHODS: Three estimators termed direct, standard IV and adjusted IV, of the phenotype-disease log odds ratio are compared through a simulation study which incorporates unmeasured confounding. The simulations are verified using formulae relating marginal and conditional estimates given in the Appendix.

RESULTS: The simulations show that the direct estimator is biased by unmeasured confounding factors and the standard IV estimator is attenuated towards the null. Under most circumstances the adjusted IV estimator has the smallest bias, although it has inflated type I error when the unmeasured confounders have a large effect.

CONCLUSIONS: In a Mendelian randomization study with a binary disease outcome the bias associated with estimating the phenotype-disease log odds ratio may be of practical importance and so estimates should be subject to a sensitivity analysis against different amounts of hypothesized confounding.

Original languageEnglish
Pages (from-to)1161 - 1168
Number of pages8
JournalInternational Journal of Epidemiology
Volume37
Issue number5
DOIs
Publication statusPublished - Oct 2008

Keywords

  • Bias (Epidemiology)
  • Computer Simulation
  • Confounding Factors (Epidemiology)
  • Data Interpretation, Statistical
  • Humans
  • Phenotype
  • Random Allocation
  • Treatment Outcome

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