Robust MR method developed for two-sample MR settings that downweights and excludes instrumental variables (IVs) with heterogeneous causal estimates that are likely invalid Cochran's Q statistic.
The method generates penalized weights based on the Cochran's Q statistic distribution to severly penalize IVs that have outlying causal effect estimates without downweighting genetic variants that are likely valid IVs. These penalized weights an also be applied to robust regression for both the inverse variance weighted (IVW) and MR-Egger methods, which are referred to as the robust and penalized approach (or robust regression with penalized weights). See also MR Robust and MR Lasso.
References
Other terms in 'Pleiotropy-robust two-sample MR methods':
- Bayes MR
- Bayesian implementation of the MR-Egger Estimator (BMRE)
- Bayesian multi-instrument Mendelian randomization (MIMR)
- Bayesian network analysis
- Causal Analysis Using Summary Effect estimates (MR-CAUSE)
- Contamination mixture models
- Generalized Summary MR (GSMR)
- Genetic Instrumental Variable (GIV)
- Hierarchical joint Analysis of Marginal summary statistics (hJAM)
- Inverse variance weighted (IVW) random effects model
- Iterative Mendelian Randomization and Pleiotropy (IMRP)
- Leave-one-out analysis
- Median-based estimate
- Mode-based estimate
- MR accounting for Correlated and Idiosyncratic Pleiotropy (MRCIP)
- MR accounting for Linkage Disequilibrium and Pleiotropy (MR-LDP)
- MR Lasso
- MR Mixture (MRMix)
- MR using Robust regression (MR Robust)
- MR with regularization
- MR-Clust
- MR-Egger regression and extensions
- MR-Link
- MR-Path
- Multivariable MR (MVMR) and extensions
- Welch-weighted Egger regression (WWER)