Evidence map›Paper›PMID 32698705›Full record

ArticleHIV research & clinical practice

Trial designs for evaluating combination HIV prevention approaches.

Ying Qing Chen, Lili Peng, Yixin Wang, Eline Appelmans, Sayan Dasgupta, Thomas R Fleming

Abstract read
In one paragraph

Article in HIV research & clinical practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Designing & Conducting Trials To Reliably Evaluate HIV Prevention Interventions.Statistical communications in infectious diseases · 2019
    Article
  2. Review
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

6 authors.

Ying Qing ChenVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0002-2726-3391
Lili PengVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Yixin WangVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Eline AppelmansVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Sayan DasguptaVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0001-5271-7247
Thomas R FlemingDepartment of Biostatistics, University of Washington, Seattle, WA, USA.

Funding

Statistical Methods for Adherence Issues in HIV Prevention ResearchR01AI121259 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2015 to 2018
$2.5M
Statistical Methods for Combination HIV Prevention ApproachesR01MH105857 · NIMH · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2014 to 2018
$2.4M
Methods for Complex Longitudinal Data in HIV/AIDS Prevention ResearchR56AI140953 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2018 to 2018
$657k
NIAID NIH HHS R01 AI121259NIAID NIH HHS R56 AI140953NIMH NIH HHS R01 MH105857
6 · The paper itself

Abstract

backgroundCombination HIV prevention approaches that include both biomedical and non-biomedical interventions often hold greater promise to improve health outcomes and reduce the risk of HIV transmission.

objectivesEvaluate the relative properties of four leading candidate trial designs - 'single-factor', 'multi-arm', 'all-in-one', and 'factorial' designs - for assessing individual and/or combination prevention intervention approaches.

methodsMonte-Carlo simulations are conducted, assuming a putative combination approach could choose its components from two candidate biomedical interventions, i.e. Treatment-as-Prevention (TasP) and Pre-exposure Prophylaxis (PrEP), and three candidate behavioral interventions, i.e. linkage-to-care, counseling, and use of condoms. Various scenarios for individual components' effect sizes, their possible interaction, and the sample size based on real clinical studies are considered.

resultsThe all-in-one and factorial designs used to assess a combination approach and the multi-arm design used to assess multiple individual components are consistently more powerful than single-factor designs. The all-in-one design is powerful when the individual components are effective without negative interaction, while the factorial design is more consistently powerful across a broad array of settings.

conclusionsThe multi-arm design is useful for evaluating single factor regimens, while the all-in-one and factorial designs are sensitive in assessing the overall efficacy when there is interest in combining individual component regimens anticipated to have complementary mechanisms. The factorial design is a preferred approach when assessing combination regimens due to its favorable power properties and since it is the only design providing direct insights about the contribution of individual components to the combination approach's overall efficacy and about potential interactions.

Indexed as

Clinical Trials as TopicResearch DesignComputer SimulationData Interpretation, StatisticalHIV InfectionsHumansMonte Carlo MethodSample SizeEfficacy trialsfactorial designHIV prevention researchmulti-component interactionsimulations

Identifiers

PMID32698705
PMCPMC7608072

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Registered trials

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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.