{"doi":"10.1111/rssa.12564","title":"Improving External Validity of Epidemiologic Cohort Analyses: a Kernel Weighting Approach","abstract":"For various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population-representativeness, which invalidates estimates of population prevalences for novel health factors only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that uses survey data as a reference to create pseudo-weights for cohorts. A jackknife variance is proposed for the KW estimates. In simulations, the KW method outperformed two existing propensity-score-based weighting methods in mean-squared error while maintaining confidence interval coverage. We applied all methods to estimating US population mortality and prevalences of various diseases from the non-representative US NIH-AARP cohort, using the sample from US-representative National Health Interview Survey (NHIS) as the reference. Assuming that the NHIS estimates are correct, the KW approach yielded generally less biased estimates compared to the existing propensity-score-based weighting methods.","journal":"Journal of the Royal Statistical Society Series A (Statistics in Society)","year":2020,"id":93243,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":28634,"name":"Barry I. Graubard","orcid":"0000-0002-6787-1105","position":1,"is_corresponding":false},{"id":237731,"name":"Hormuzd A. Katki","orcid":"0000-0003-2002-0095","position":2,"is_corresponding":false},{"id":465730,"name":"Yan Li","orcid":"0000-0001-8241-7464","position":3,"is_corresponding":false},{"id":465729,"name":"Lingxiao Wang","orcid":"0000-0002-3463-615X","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-18T22:30:54.208860Z","pmid":"33071484","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,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}