{"doi":"10.17615/g81d-h634","title":"Prevalence estimation and validation of new instruments in psychiatric research: An application of latent class analysis and sensitivity analysis.","abstract":"Prevalence and validation studies rely on imperfect reference standard (RS) diagnostic instruments which can bias prevalence and test characteristic estimates. We illustrate two methods to account for RS misclassification. Latent class analysis (LCA) combines information from multiple imperfect measures of an unmeasurable “latent” condition to estimate sensitivity (Se) and specificity (Sp) of each measure. Simple algebraic sensitivity analysis (SA) uses researcher-specified RS misclassification rates to correct prevalence and test characteristic estimates, and can succinctly summarize a range of scenarios with Monte Carlo simulation. We applied LCA to a validation study of a new substance use disorder (SUD) screener and a larger prevalence study. A traditional validation study analysis that assumed an error-free RS (SCID) estimated the screener had 86% Se/75% Sp. Validation study estimates from LCA were 91% Se/81% Sp (screener) and 73% Se/98% Sp (SCID). SA in the prevalence study suggested the prevalence of SUD was underestimated by 22% by assuming the SCID to be error-free. LCA and SA can assist investigators in relaxing the unrealistic assumption of perfect RSs in reporting prevalence and validation study results.","journal":"UNC Libraries","year":2020,"id":143535,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9545,"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":366445,"name":"Bradley N. Gaynes","orcid":"0000-0002-8283-5030","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:17:38.432803Z","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,"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":[]}