{"doi":"10.1002/gepi.21965","title":"Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator","abstract":"Developments in genome-wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse-variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite-sample Type 1 error rates than the inverse-variance weighted method, and is complementary to the recently proposed MR-Egger (Mendelian randomization-Egger) regression method. In analyses of the causal effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol on coronary artery disease risk, the inverse-variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR-Egger regression methods suggest a null effect of high-density lipoprotein cholesterol that corresponds with the experimental evidence. Both median-based and MR-Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.","journal":"Genetic Epidemiology","year":2016,"id":12651,"datarank":10.618460661160789,"base_score":9.156306483483696,"endowment":9.156306483483696,"self_citation_contribution":1.3734459725225547,"citation_network_contribution":9.245014688638234,"self_endowment_contribution":1.3734459725225547,"citer_contribution":9.245014688638234,"corpus_percentile":90.7,"corpus_rank":1178,"citation_count":9473,"citer_count":113,"citers_with_citation_signal":113,"citers_with_endowment":113,"datacite_reuse_total":0,"is_dataset":false,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2016-04-07","authors":[{"id":1528,"name":"George Davey Smith","orcid":"0000-0002-1407-8314","position":1,"is_corresponding":false},{"id":78832,"name":"Philip C Haycock","orcid":"0000-0001-5001-3350","position":2,"is_corresponding":false},{"id":78822,"name":"Stephen Burgess","orcid":"0000-0001-5365-8760","position":3,"is_corresponding":false},{"id":78823,"name":"Jack Bowden","orcid":"0000-0003-2628-3304","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}