{"doi":"10.1167/tvst.14.9.24","title":"A Bayesian Hierarchical Longitudinal Model for Estimation of Central Visual Field Rates of Change in Glaucoma","abstract":"Purpose: Individual visual field (VF) sensitivities become unreliable at threshold sensitivities of 19 dB or less, limiting glaucoma monitoring. We evaluated longitudinal variability of central 10° VF measurements based on baseline sensitivity using a Bayesian hierarchical model. Methods: We included 124 glaucoma patients (124 eyes) with central or moderate-to-advanced VF damage, more than 2 years follow-up, and more than 4 central 10-2 VF tests. A Bayesian linear model estimated pointwise change rates, compared with simple linear regression (SLR). Simulations modeled average (-0.21 dB/year) and benchmark (-0.5 dB/year) slopes with residual standard deviations (SD) of 2, 4, 7, or 10 dB. Outcomes included pointwise residual SDs and proportions of significant slopes in cohort and simulations. Results: The average baseline 10-2 VF mean deviation, follow-up time, and median VF tests were 8.4 ± 5.4 dB, 4.6 ± 0.8 years, and 9 VF tests (range, 4-12 VF tests), respectively. The mean global slopes for Bayesian and SLR models were -0.21 and -0.36 dB/year. Residual SDs were markedly higher when baseline threshold sensitivities was 5 to 20 dB compared with 25 dB or greater. The Bayesian model identified more significant negative slopes, particularly at points with residual SD of less than 4 dB, relative to SLR. Conclusions: When baseline pointwise sensitivity is 5 to 20 dB, residual variability is very large, substantially reducing the ability to detect glaucoma progression. Translational Relevance: Visual field locations with sensitivity near or less than 20 dB demonstrate markedly greater variability over time; thus, excluding these points from visual field algorithms or analytical models could improve efficiency in detecting perimetric progression.","journal":"Translational Vision Science & Technology","year":2025,"id":546910,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7541,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":539589,"name":"Erica Su","orcid":"0000-0003-0402-9938","position":1,"is_corresponding":false},{"id":1074668,"name":"Sajad Besharati","orcid":"0000-0002-6520-0877","position":2,"is_corresponding":false},{"id":1436863,"name":"Abraham Liu","orcid":null,"position":3,"is_corresponding":false},{"id":316298,"name":"Simon K. Law","orcid":null,"position":4,"is_corresponding":false},{"id":315554,"name":"Anne L. Coleman","orcid":"0000-0002-0807-5176","position":5,"is_corresponding":false},{"id":6862,"name":"Joseph Caprioli","orcid":"0000-0002-2383-7263","position":6,"is_corresponding":false},{"id":519082,"name":"Robert E. Weiss","orcid":"0000-0003-3648-8522","position":7,"is_corresponding":false},{"id":315555,"name":"Kouros Nouri‐Mahdavi","orcid":"0000-0001-9403-8904","position":8,"is_corresponding":false},{"id":316297,"name":"Vahid Mohammadzadeh","orcid":null,"position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T02:53:36.567932Z","pmid":"40956040","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":[]}