Evidence map›Paper›PMID 33782485›Full record

ArticleScientific reports2021

Use of propensity score matching to create counterfactual group to assess potential HIV prevention interventions.

Andrew Abaasa, Yunia Mayanja, Gershim Asiki, Matt A Price, Patricia E Fast, Eugene Ruzagira, Pontiano Kaleebu, Jim Todd

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 35% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
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

8 authors at 4 institutions in 5 countries.

Andrew AbaasaMRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda. andrew.abaasa@mrcuganda.org.
Yunia MayanjaMRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda.
Gershim AsikiAfrican Population and Health Research Center, Nairobi, Kenya.
Matt A PriceIAVI, New York, USA.
Patricia E FastIAVI, New York, USA.
Eugene RuzagiraMRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda.
Pontiano KaleebuMRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda.
Jim ToddLondon School of Hygiene and Tropical Medicine, London, UK.
Uganda Virus Research Institute · UGInternational AIDS Vaccine Initiative · USLondon School of Hygiene & Tropical Medicine · GBAfrican Population and Health Research Center · KE

Funding

Medical Research CouncilPEPFAR
6 · The paper itself

Abstract

The design of HIV prevention trials in the context of effective HIV preventive methods is a challenge. Alternate designs, including using non-randomised 'observational control arms' have been proposed. We used HIV simulated vaccine efficacy trials (SiVETs) to show pitfalls that may arise from using such observational controls and suggest how to conduct the analysis in the face of the pitfalls. Two SiVETs were nested within previously established observational cohorts of fisherfolk (FF) and female sex workers (FSW) in Uganda. SiVET participants received a licensed Hepatitis B vaccine in a schedule (0, 1 and 6 months) similar to that for a possible HIV vaccine efficacy trial. All participants received HIV counselling and testing every quarter for one year to assess HIV incidence rate ratio (IRR) between SiVET and non-SiVET (observational data). Propensity scores, conditional on baseline characteristics were calculated for SiVET participation and matched between SiVET and non-SiVET in the period before and during the SiVET study. We compared IRR before and after propensity score matching (PSM). In total, 3989 participants were enrolled into observational cohorts prior to SiVET, (1575 FF prior to Jul 2012 and 2414 FSW prior to Aug 2014). SiVET enrolled 572 participants (Jul 2012 to Apr 2014 in FF and Aug 2014 to Apr 2017 in FSW), with 953 non-SiVET participants observed in the SiVET concurrent period and 2928 from the pre-SiVET period (before Jul 2012 in FF or before Apr 2014 in FSW). Imbalances in baseline characteristics were observed between SiVET and non-SiVET participants in both periods before PSM. Similarly, HIV incidence was lower in SiVET than non-SiVET; SiVET-concurrent period, IRR = 0.59, 95% CI 0.31-0.68, p = 0.033 and pre-SiVET period, IRR = 0.77, 95% CI 0.43-1.29, p = 0.161. After PSM, participants baseline characteristics were comparable and there were minimal differences in HIV incidence between SiVET and non-SiVET participants. The process of screening for eligibility for efficacy trial selects participants with baseline characteristics different from the source population, confounding any observed differences in HIV incidence. Propensity score matching can be a useful tool to adjust the imbalance in the measured participants' baseline characteristics creating a counterfactual group to estimate the effect of interventions on HIV incidence.

Indexed as

Propensity ScoreAdolescentAdultAIDS VaccinesFemaleHIVHIV InfectionsHumansIncidenceMaleMass ScreeningMiddle AgedSex WorkersUgandaYoung AdultAIDS Vaccines

Identifiers

PMID33782485
PMCPMC8007631
OpenAlexW3109386661

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.