{"doi":"10.1101/2021.02.04.21251133","title":"Vaccine Efficacy at a Point in Time","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Vaccine trials are generally designed to assess efficacy on clinical disease. The vaccine effect on infection, while important both as a proxy for transmission and to describe a vaccine’s total effects, requires frequent longitudinal sampling to capture all infections. Such sampling may not always be feasible. A logistically easy approach is to collect a sample to test for infection at a regularly scheduled visit. Such point or cross-sectional sampling does not permit estimation of classic vaccine effiacy on infection, as long duration infections are sampled with higher probability. Building on work by Rinta-Kokko\n                  <jats:italic>and others</jats:italic>\n                  (2009) we evaluate proxies of the vaccine effect on transmission at a point in time; the vaccine efficacy on prevalent infection and on prevalent viral load, VE\n                  <jats:sub>\n                    <jats:italic>PI</jats:italic>\n                  </jats:sub>\n                  and VE\n                  <jats:sub>\n                    <jats:italic>PV L</jats:italic>\n                  </jats:sub>\n                  , respectively. Longer infections with higher viral loads should have more transmission potential and prevalent vaccine efficacy naturally captures this aspect. We apply a proportional hazards model for infection risk and show how these metrics can be estimated using longitudinal or cross-sectional sampling. We also introduce regression models for designs with multiple cross-sectional sampling. The methods are evaluated by simulation and a phase III vaccine trial with PCR cross-sectional sampling for subclinical infection is analyzed.\n                </jats:p>","journal":null,"year":null,"id":595075,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":399107,"name":"Michael P. Fay","orcid":"0000-0002-8643-9625","position":1,"is_corresponding":false},{"id":390353,"name":"Dean Follmann","orcid":"0000-0003-4073-0393","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Vaccine Efficacy at a Point in Time","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Vaccine trials are generally designed to assess efficacy on clinical disease. The vaccine effect on infection, while important both as a proxy for transmission and to describe a vaccine’s total effects, requires frequent longitudinal sampling to capture all infections. Such sampling may not always be feasible. A logistically easy approach is to collect a sample to test for infection at a regularly scheduled visit. Such point or cross-sectional sampling does not permit estimation of classic vaccine effiacy on infection, as long duration infections are sampled with higher probability. Building on work by Rinta-Kokko\n                  <jats:italic>and others</jats:italic>\n                  (2009) we evaluate proxies of the vaccine effect on transmission at a point in time; the vaccine efficacy on prevalent infection and on prevalent viral load, VE\n                  <jats:sub>\n                    <jats:italic>PI</jats:italic>\n                  </jats:sub>\n                  and VE\n                  <jats:sub>\n                    <jats:italic>PV L</jats:italic>\n                  </jats:sub>\n                  , respectively. Longer infections with higher viral loads should have more transmission potential and prevalent vaccine efficacy naturally captures this aspect. We apply a proportional hazards model for infection risk and show how these metrics can be estimated using longitudinal or cross-sectional sampling. We also introduce regression models for designs with multiple cross-sectional sampling. The methods are evaluated by simulation and a phase III vaccine trial with PCR cross-sectional sampling for subclinical infection is analyzed.\n                </jats:p>","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W3126835707","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2021,"count":4},{"year":2022,"count":1},{"year":2023,"count":2}],"oa_status":"green","license":null,"oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2021/02/06/2021.02.04.21251133.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2021/02/06/2021.02.04.21251133.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2021.02.04.21251133","host_type":"publisher"},{"url":"https://doi.org/10.1101/2021.02.04.21251133","host_type":"repository"}],"fields_of_study":["SARS-CoV-2 and COVID-19 Research","Viral Infections and Immunology Research","SARS-CoV-2 detection and testing"],"mesh_terms":[],"keywords":["Vaccine trial","Vaccine efficacy","Sampling (signal processing)","Transmission (telecommunications)","Subclinical infection","Viral load","Medicine","Statistics","Proxy (statistics)","Cross-sectional study","Vaccination","Immunology","Virology","Computer science","Mathematics","Pathology","Human immunodeficiency virus (HIV)"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T16:30:59.434616Z","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":[]}