Evidence map›Paper›PMID 35880999›Full record

ArticleStatistical communications in infectious diseases2021

Sample size calculation for active-arm trial with counterfactual incidence based on recency assay.

Fei Gao, David V Glidden, James P Hughes, Deborah J Donnell

Open access · greenAbstract read
In one paragraph

Article in Statistical communications in infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.9field-weighted citation impact, top 22% of its field
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

15 citing papers in PubMed, 16 citations in OpenAlex.

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  11. Study design approaches for future active-controlled HIV prevention trials.Statistical communications in infectious diseases · 2024
    Article
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  13. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Fei GaoFred Hutchinson Cancer Research Center, Seattle, USA.
David V GliddenDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
James P HughesDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Deborah J DonnellVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Fred Hutch Cancer Center · USUniversity of California, San Francisco · USUniversity of Washington · US

Funding

SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
STATISTICAL ISSUES IN AIDS RESEARCHR37AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI JAMES P HUGHES · 1999 to 2026
$9.8M
STATISTICAL ISSUES IN AIDS RESEARCHR01AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI HUGHES, JAMES P · 1989 to 2018
$5.3M
Statistical Methods for Evaluating and Guiding Implementation of New HIV Prevention StrategiesR56AI143418 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI JANES, HOLLY · 2019 to 2022
$816k
Statistical methods for clinical trials of novel PrEP agentsR01AI143357 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLIDDEN, DAVID V. · 2018 to 2021
$483k
NIAID NIH HHS R01 AI029168NIAID NIH HHS R01 AI143357NIAID NIH HHS R37 AI029168NIAID NIH HHS R56 AI143418NIAID NIH HHS UM1 AI068617
6 · The paper itself

Abstract

Objectives: The past decade has seen tremendous progress in the development of biomedical agents that are effective as pre-exposure prophylaxis (PrEP) for HIV prevention. To expand the choice of products and delivery methods, new medications and delivery methods are under development. Future trials of non-inferiority, given the high efficacy of ARV-based PrEP products as they become current or future standard of care, would require a large number of participants and long follow-up time that may not be feasible. This motivates the construction of a counterfactual estimate that approximates incidence for a randomized concurrent control group receiving no PrEP. Methods: We propose an approach that is to enroll a cohort of prospective PrEP users and aug-ment screening for HIV with laboratory markers of duration of HIV infection to indicate recent infections. We discuss the assumptions under which these data would yield an estimate of the counterfactual HIV incidence and develop sample size and power calculations for comparisons to incidence observed on an investigational PrEP agent. Results: We consider two hypothetical trials for men who have sex with men (MSM) and transgender women (TGW) from different regions and young women in sub-Saharan Africa. The calculated sample sizes are reasonable and yield desirable power in simulation studies. Conclusions: Future one-arm trials with counterfactual placebo incidence based on a recency assay can be conducted with reasonable total screening sample sizes and adequate power to determine treatment efficacy.

Indexed as

counterfactual placebo incidenceHIV preventionpre-exposure prophylaxisrecency assaysample size calculation

Identifiers

PMID35880999
PMCPMC8865397
OpenAlexW3212058567

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.