MR with a time-to-event outcome

Two-stage approach for causal estimation with time-to-event outcomes, where 2nd stage regression is substituted with either a Cox proportional hazard, or additive hazard regression. 

Genetic variants must satisfy the IV assumptions. Outcome must be rare for Cox model to be valid. Censoring must be independent of mortality. Model specification must be correct (i.e., additive hazard). Confounding acts linearly on additive hazard scale. However, there is no consensus about the gold standard way of dealing with time-to-event data within an MR context. 

References

• Vansteelandt S, Dukes O, Martinussen T. Survivor bias in Mendelian randomization analysis. Biostatistics 2018;19:426-443.
• Tchetgen Tchetgen EJ, Walter S, Vansteelandt S, Martinussen T, Glymour M. Instrumental variable estimation in a survival context. Epidemiology (Cambridge, Mass) 2015;26:402-410.

Other terms in 'One-sample MR methods':

• Generalized Method of Moments (GMM) estimator
• Non-parametric methods with bounds of causal effect
• Polygenic risk score approach
• Semi-parametric structural mean model (SMMs) for binary outcomes
• TSLS with binary outcome
• Two-stage least squares (TSLS)