Yield of positive results in FSW was not significantly different between HIVST and SoC. There was no difference between HIVST and SoC in regard to positivity among tested overall (RR = 0.91 95% CI 0.73, 1.15) in sensitivity analysis of positivity among randomised HIVST identified significantly more HIV infections among MSM and trans people (RR = 2.21 95% CI 1.20, 4.08) and in online/mail distribution systems (RR = 2.21 95% CI 1.14, 4.32). For MSM and small numbers of trans people, HIVST increased the mean number of HIV tests by 2.56 over follow-up (mean difference = 2.56 95% CI 1.24, 3.88). Support components were highly diverse and ranged from helplines to training and supervision.
Service delivery models included facility-based, online/mail and peer distribution. These included 9679 participants, of whom 5486 were men who have sex with men (MSM), 72 were trans people and 4121 were female sex workers. ResultsĪfter screening 5909 titles and abstracts, we identified 10 RCTs which reported on testing outcomes. Random effects meta-analyses were conducted, and pooled effect estimates were assessed along with other evidence characteristics to determine the overall strength of the evidence using GRADE methodology. We extracted study characteristic and outcome data and conducted risk of bias assessments using the Cochrane ROB tool version 1. We did a systematic review of randomised controlled trials (RCTs) which compared HIVST to standard HIV testing in key populations, published from 1 January 2006 to 4 June 2019 in PubMed, Embase, Global Index Medicus, Social Policy and Practice, PsycINFO, Health Management Information Consortium, EBSCO CINAHL Plus, Cochrane Library and Web of Science. We compared the effects of HIVST to standard HIV testing services to understand which service delivery models are effective for key populations.
Comprehensive meta analysis for screening tests update#
He is currently particularly interested in methods for visual communication of information and its application to synthesis methods.We update a previous systematic review to inform new World Health Organization HIV self-testing (HIVST) recommendations. Her research interests include network meta-analysis, individual participant data meta-analysis, synthesis of time-to-event and continuous outcomes and diagnostic test accuracy meta-analysis.Īlex Sutton has had a long-standing interest in methodology for evidence synthesis and has worked on developing methods for publication bias, diagnostic test accuracy meta-analysis and network meta-analysis. Suzanne Freeman is an NIHR Research Fellow and member of the NIHR Complex Reviews Support Unit. Part 3: Simulation study, limitations and conclusions
The webinar was delivered in July 2020 and below you will find the videos from the webinar, together with accompanying slides to download. Although there is some statistical content, concepts are explained visually where possible keeping much of the material accessible. These videos, originally part of the Cochrane Learning Live webinar series, are highly relevant to reviewers, editors and statisticians with interests in dealing with bias in meta-analysis. Through application to a motivating collection of meta-analyses of post-operative analgesics, and simulation studies, we will show that Egger’s test is potentially misleading for continuous outcomes and a test which regresses the residuals from a meta-regression model, including baseline risk as a study-level covariate, against inverse sample size has better statistical properties. When this is the case, funnel plots can appear highly asymmetric, even when publication bias is not present, since correlations between outcome and both effect size and its standard error exist. Comparative continuous outcomes are commonly measured on an absolute (mean) difference scale, and it is not uncommon for the magnitude of effect to be related to response in the control arm (i.e. The performance of Egger’s and related tests has been extensively studied for binary outcomes, but not for continuous ones. Egger’s test is commonly used to assess potential publication bias in a meta-analysis via funnel plot asymmetry (Egger’s test is a linear regression of the intervention effect estimates on their standard errors weighted by their inverse variance).
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