Evidence map›Paper›PMID 34174097›Full record

ArticleBiometrics2022

Estimating vaccine efficacy over time after a randomized study is unblinded.

Anastasios A Tsiatis, Marie Davidian

Abstract read
In one paragraph

Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Trial
  2. Review
  3. Designs for Vaccine Studies.Annual review of statistics and its application · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Anastasios A TsiatisDepartment of Statistics, North Carolina State University, Raleigh, NC, USA.
Marie DavidianDepartment of Statistics, North Carolina State University, Raleigh, NC, USA.ORCID 0000-0002-9202-6341

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in the development of vaccines against the virus and the launch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine efficacy (VE). Trials of vaccine candidates using mRNA delivery systems developed by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%, leading the US Food and Drug Administration to issue Emergency Use Authorizations and subsequent widespread administration of the vaccines. As the trials continue, a key issue is the possibility that VE may wane over time. Ethical considerations dictate that trial participants be unblinded and those randomized to placebo be offered study vaccine, leading to trial protocol amendments specifying unblinding strategies. Crossover of placebo subjects to vaccine complicates inference on waning of VE. We focus on the particular features of the Moderna trial and propose a statistical framework based on a potential outcomes formulation within which we develop methods for inference on potential waning of VE over time and estimation of VE at any postvaccination time. The framework clarifies assumptions made regarding individual- and population-level phenomena and acknowledges the possibility that subjects who are more or less likely to become infected may be crossed over to vaccine differentially over time. The principles of the framework can be adapted straightforwardly to other trials.

Indexed as

COVID-19COVID-19 VaccinesHumansPandemicsRandomized Controlled Trials as TopicResearch DesignSARS-CoV-2Vaccine EfficacyCOVID-19 Vaccinescrossoverinverse probability weightingpotential outcomesrandomized phase 3 vaccine trialwaning vaccine efficacy

Identifiers

PMID34174097
PMCPMC8444907

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.