Evidence map›Paper›PMID 30326736›Full record

ArticleClinical trials (London, England)2019

Generalizing the per-protocol treatment effect: The case of ACTG A5095.

Haidong Lu, Stephen R Cole, H Irene Hall, Enrique F Schisterman, Tiffany L Breger, Jessie K Edwards, Daniel Westreich

Open access · bronzeAbstract read
In one paragraph

Article in Clinical trials (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Trial
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  3. Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
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  5. Article
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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

7 authors at 3 institutions in 1 country.

Haidong Lu1 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Stephen R Cole1 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
H Irene Hall2 Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Enrique F Schisterman3 Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Tiffany L Breger1 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Jessie K Edwards1 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Daniel Westreich1 Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
University of North Carolina at Chapel Hill · USEunice Kennedy Shriver National Institute of Child Health and Human Development · USNational Center for HIV/AIDS Viral Hepatitis STD and TB Prevention · US

Funding

Virology, Immunology, and Microbiology CoreP30AI050410 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVID M. MARGOLIS · 2001 to 2026
$76.9M
UNC MACS/WIHS Combined Cohort Study Clinical Research SiteU01HL146194 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Michael Bradley Drummond, Michelle Floris-Moore · 2019 to 2026
$23.2M
From patients to policy: innovative epidemiology for implementation scienceDP2HD084070 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WESTREICH, DANIEL J · 2014 to 2014
$2.3M
Structural models for treatment and exposure effects in clinical HIV cohortsR01AI100654 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COLE, STEPHEN R · 2013 to 2016
$1.6M
Comparative effectiveness of tailored HIV treatment plans and mortalityK01AI125087 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI EDWARDS, JESSIE · 2017 to 2021
$586k
NHLBI NIH HHS U01 HL146194NIAID NIH HHS K01 AI125087NIAID NIH HHS P30 AI050410NIAID NIH HHS R01 AI100654NICHD NIH HHS DP2 HD084070
6 · The paper itself

Abstract

backgroundIntention-to-treat comparisons of randomized trials provide asymptotically consistent estimators of the effect of treatment assignment, without regard to compliance. However, decision makers often wish to know the effect of a per-protocol comparison. Moreover, decision makers may also wish to know the effect of treatment assignment or treatment protocol in a user-specified target population other than the sample in which the trial was fielded. Here, we aimed to generalize results from the ACTG A5095 trial to the US recently HIV-diagnosed target population.

methodsWe first replicated the published conventional intention-to-treat estimate (2-year risk difference and hazard ratio) comparing a four-drug antiretroviral regimen to a three-drug regimen in the A5095 trial. We then estimated the intention-to-treat effect that accounted for informative dropout and the per-protocol effect that additionally accounted for protocol deviations by constructing inverse probability weights. Furthermore, we employed inverse odds of sampling weights to generalize both intention-to-treat and per-protocol effects to a target population comprising US individuals with HIV diagnosed during 2008-2014.

resultsOf 761 subjects in the analysis, 82 dropouts (36 in the three-drug arm and 46 in the four-drug arm) and 59 protocol deviations (25 in the three-drug arm and 34 in the four-drug arm) occurred during the first 2 years of follow-up. A total of 169 subjects incurred virologic failure or death. The 2-year risks were similar both in the trial and in the US HIV-diagnosed target population for estimates from the conventional intention-to-treat, dropout-weighted intention-to-treat, and per-protocol analyses. In the US target population, the 2-year conventional intention-to-treat risk difference (unit: %) for virologic failure or death comparing the four-drug arm to the three-drug arm was -0.4 (95% confidence interval: -6.2, 5.1), while the hazard ratio was 0.97 (95% confidence interval: 0.70, 1.34); the 2-year risk difference was -0.9 (95% confidence interval: -6.9, 5.3) for the dropout-weighted intention-to-treat comparison (hazard ratio = 0.95, 95% confidence interval: 0.68, 1.32) and -0.7 (95% confidence interval: -6.7, 5.5) for the per-protocol comparison (hazard ratio = 0.96, 95% confidence interval: 0.69, 1.34).

conclusionNo benefit of four-drug antiretroviral regimen over three-drug regimen was found from the conventional intention-to-treat, dropout-weighted intention-to-treat or per-protocol estimates in the trial sample or target population.

Indexed as

Sustained Virologic ResponseAdultAnti-Retroviral AgentsClinical ProtocolsFemaleHIV InfectionsHumansIntention to Treat AnalysisMaleMiddle AgedViral LoadAnti-Retroviral Agentsantiretroviral therapycausalityclinical trialexternal validitygeneralizabilityHIV/AIDSintention-to-treat effectinverse probability weightingper-protocol effectvirologic failure

Identifiers

PMID30326736
PMCPMC6693502
OpenAlexW2897235331

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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.