Interpretability and robustness of sieve analysis models for assessing HIV strain variations in vaccine efficacy.
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.
Gilbert
P B
PB
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. pgilbert@hsp.harvard.edu
D016915:AIDS Vaccines / Q000276:immunology* / Q000627:therapeutic use; D017326:Clinical Trials, Phase III as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000276:immunology / Q000517:prevention & control*; D015497:HIV-1 / Q000145:classification / Q000276:immunology*; D006801:Humans; D016015:Logistic Models; D018448:Models, Immunological; D012680:Sensitivity and Specificity; D013785:Thailand; D014611:Vaccination / Q000706:statistics & numerical data
D016915:AIDS Vaccines