Ad-hoc method for causal estimation with binary outcomes, where the second stage regression is substituted with a logistic regression.
Genetic variants being used as instrumental variables (IVs) must satisfy the MR assumptions. Genetic variants each exert a small effect on the exposure in order to approximately convey the same causal effect. Non-collapsibility of odds ratios means that the population average effects are estimated rather than individual-level causal effects.
References
- Burgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. European journal of epidemiology 2018; 33: 947-952.
- Bowden J, Vansteelandt S. Mendelian randomization analysis of case-control data using structural mean models. Statistics in Medicine 2011; 30: 678-694.
- Palmer TM, Thompson JR, Tobin MD, Sheehan NA, Burton PR. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses. International Journal of Epidemiology 2008; 37: 1161-1168.
Other terms in 'One-sample MR methods':
- Generalized Method of Moments (GMM) estimator
- MR with a time-to-event outcome
- Non-parametric methods with bounds of causal effect
- Polygenic risk score approach
- Structural Mean Models (SMMs)
- Two-stage least squares (TSLS)
- Two-stage predictor substitution estimators
- Two-stage residual inclusion estimators
- Within-family MR