{"doi":"10.1111/biom.13542","title":"Discussion on “Estimating vaccine efficacy over time after a randomized study is unblinded” by Anastasios A. Tsiatis and Marie Davidian","abstract":"A recent publication by Tsiatis and Davidian addresses the challenge of evaluating longer term efficacy of a COVID-19 vaccine in the context of a blinded, placebo-controlled phase 3 vaccine trial when, based on interim efficacy results, participants are unblinded and placebo recipients are offered vaccine mid-study. How can long-term durability of vaccine efficacy be evaluated in the absence of concurrent control data throughout follow-up? Importantly, the challenge being addressed is a consequence of highly successful science and public policy—it is attributable to the high efficacy seen to-date for COVID-19 vaccines and the rapid rollout of effective vaccines under Emergency Use Authorization mechanisms. The authors' proposed approach is informed by the first author's deep engagement with the ongoing US-government-funded phase 3 COVID-19 vaccine trials. Tsiatis and Davidian, hereafter “TD,” focus specifically on unblinded placebo crossover, wherein after interim efficacy results participants are unblinded as to initial randomization, and those randomized to placebo and desiring vaccine are provided it. Participants are also free to ask to be unblinded to randomization assignment at any point during follow-up, for example, to access outside-study vaccination. Follow-up of participants crossing over to vaccine both on-study or outside-study continues to ensure that data are collected to evaluate long-term vaccine efficacy. Consistent with all published phase 3 COVID-19 vaccine trials to-date, TD target estimation of vaccine efficacy as measured by the reduction in incidence of COVID-19 disease under vaccine versus placebo. This incidence is allowed to depend on the time that has accrued since first vaccination and as realized under a blinded trial design. They propose a potential-outcomes-based framework for making inference about vaccine efficacy and explore bias in standard methods of estimation attributable to the unblinded crossover design feature. The specific issue TD are concerned with is potential bias in estimation due to informative unblinding, which occurs when individuals with different risk characteristics cross over to vaccine at different points in time. This issue is especially critical for unblinding due to outside-study vaccination, where motivation for pursuing vaccination and access to vaccine differs across subpopulations based on risk of COVID-19 disease. The issue may also apply to blinded crossover, whereby original placebo recipients are crossed over to vaccine and original vaccine recipients are given placebo, but, because timing of blinded crossover is typically dictated by design, informative unblinding is much less likely in this scenario. To address this issue, TD develop a framework that defines the individual-level trial data under unblinded crossover of a phase 3 COVID-19 vaccine efficacy trial. As in prior methodological work on these trials, TD base their analysis on calendar time to appropriately align participants enrolled in a staggered fashion with regard to the secular trends in SARS-CoV-2 incidence. They model the COVID-19 disease process by parameters describing the population prevalence, contact rate parameters measuring population mixing, and COVID-19 acquisition probabilities. An estimating equations approach that leverages inverse probability weighting to adjust for bias due to informative timing of enrollment and unblinding is used for estimation. TD's approach allows the population prevalence to vary in space and time. The contact rate parameters are allowed to depend on baseline covariates, vaccine receipt, and knowledge thereof, for example, allowing for more cautious behavior before full vaccination than after and before knowledge of vaccine receipt. Probability of acquiring COVID-19 is allowed to depend on calendar time, vaccine receipt, time since first vaccine dose, and baseline covariates; however, the ratio of conditional acquisition probabilities given covariates is assu","journal":"Biometrics","year":2021,"id":212722,"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":0.958,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":415504,"name":"Fei Gao","orcid":"0000-0001-6797-5468","position":1,"is_corresponding":false},{"id":305710,"name":"Alex Luedtke","orcid":"0000-0002-9936-3236","position":2,"is_corresponding":false},{"id":30696,"name":"Holly Janes","orcid":"0000-0002-3237-984X","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-18T23:52:27.434608Z","pmid":"34492117","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":[]}