Literature DB >> 11169601

Interpretability and robustness of sieve analysis models for assessing HIV strain variations in vaccine efficacy.

P B Gilbert1.   

Abstract

From data on HIV-1 characteristics measured on viruses isolated from vaccinated and unvaccinated persons infected while enrolled in preventive HIV-1 vaccine trials, interpretable inferences into strain variations of vaccine efficacy can be made with recently developed sieve analysis models. Four assumptions are needed for the parameters in these models to have meaningful interpretations in terms of vaccine-induced reductions in strain-specific per-contact transmission probabilities: (A1) vaccination impacts each strain-specific transmission probability homogeneously in vaccinated persons (leaky vaccine effect); (A2) for each strain biological susceptibility to infection given exposure is homogeneous among vaccinated trial participants and among unvaccinated trial participants; (A3) the distribution of exposure is equal in vaccinated and unvaccinated trial participants; (A4) the relative prevalence of circulating HIV-1 strains during the trial follow-up period is constant. Through theoretical considerations and simulations of an ongoing phase III HIV-1 vaccine efficacy trial in Bangkok, we evaluate the importance and necessity of these assumptions. We show that the models still provide estimates of biologically interpretable parameters when A1 is violated, but with bias the extent to which vaccine protection is heterogeneous. We also show that the models are highly robust to departures from A4, with implication that the time-independent models are adequate for applications. In addition, we suggest extensions of the sieve analysis models which incorporate random effects that account for unmeasured heterogeneity in infection risk. With these mixed models, usefully interpretable strain-specific vaccine efficacy parameters can be estimated without requiring A2. The conclusion is that A3, which is justified by randomization and blinding, is the essential assumption for the sieve models to provide reliable interpretable inferences into strain variations in vaccine efficacy. Copyright 2001 John Wiley & Sons, Ltd.

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Year:  2001        PMID: 11169601     DOI: 10.1002/1097-0258(20010130)20:2<263::aid-sim660>3.0.co;2-1

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  5 in total

1.  Sieve analysis using the number of infecting pathogens.

Authors:  Dean Follmann; Chiung-Yu Huang
Journal:  Biometrics       Date:  2017-12-14       Impact factor: 2.571

Review 2.  Genetic diversity and malaria vaccine design, testing and efficacy: preventing and overcoming 'vaccine resistant malaria'.

Authors:  S L Takala; C V Plowe
Journal:  Parasite Immunol       Date:  2009-09       Impact factor: 2.280

3.  Assessing vaccine effects in repeated low-dose challenge experiments.

Authors:  Michael G Hudgens; Peter B Gilbert
Journal:  Biometrics       Date:  2009-12       Impact factor: 2.571

4.  Combining Viral Genetics and Statistical Modeling to Improve HIV-1 Time-of-infection Estimation towards Enhanced Vaccine Efficacy Assessment.

Authors:  Raabya Rossenkhan; Morgane Rolland; Jan P L Labuschagne; Roux-Cil Ferreira; Craig A Magaret; Lindsay N Carpp; Frederick A Matsen Iv; Yunda Huang; Erika E Rudnicki; Yuanyuan Zhang; Nonkululeko Ndabambi; Murray Logan; Ted Holzman; Melissa-Rose Abrahams; Colin Anthony; Sodsai Tovanabutra; Christopher Warth; Gordon Botha; David Matten; Sorachai Nitayaphan; Hannah Kibuuka; Fred K Sawe; Denis Chopera; Leigh Anne Eller; Simon Travers; Merlin L Robb; Carolyn Williamson; Peter B Gilbert; Paul T Edlefsen
Journal:  Viruses       Date:  2019-07-03       Impact factor: 5.048

5.  Leaky vaccines protect highly exposed recipients at a lower rate: implications for vaccine efficacy estimation and sieve analysis.

Authors:  Paul T Edlefsen
Journal:  Comput Math Methods Med       Date:  2014-05-07       Impact factor: 2.238

  5 in total

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