Biomarkers that predict the efficacy of treatment can potentially improve clinical outcomes and decrease medical costs by allowing treatment to be provided only to those most likely to benefit. We consider the design of a randomized clinical trial in which one objective is to evaluate a treatment selection marker. The marker may be measured prospectively or retrospectively using samples collected at baseline. We describe and contrast criteria around which the trial can be designed. An existing approach focuses on determining if there is a statistical interaction between the marker and treatment. We propose three alternative approaches based on estimating clinically relevant measures of improvement in outcomes with use of the marker. Importantly, our approaches accommodate the common scenario in which the marker-based rule for recommending treatment is developed with data from the trial. Sample sizes are calculated for powering a trial to assess these criteria in the context of adjuvant chemotherapy for the treatment of estrogen-receptor-positive, node-positive breast cancer. In this example, we find that larger sample sizes are generally required for assessing clinical impact than for simply evaluating if there is a statistical interaction between marker and treatment. We also find that retrospectively selecting a case-control subset of subjects for marker evaluation can lead to large efficiency gains, especially if cases and controls are matched on treatment assignment.
treatment selection; biomarker; randomized controlled trial; study design; statistical interaction; predictive marker
We developed and validated a hybrid risk classifier combining serum markers and epidemiologic risk factors to identify post-menopausal women at elevated risk for invasive fallopian tube, primary peritoneal, and ovarian epithelial carcinoma.
To select epidemiologic risk factors for use in the classifier, Cox proportional hazards analyses were conducted using 74,786 Women’s Health Initiative (WHI) Observational Study (OS) participants. To construct a combination classifier, 210 WHI OS cases and 536 matched controls with serum marker measurements were analyzed; validation employed 143 cases and 725 matched controls from the WHI Clinical Trial (CT) with similar data.
Analyses identified a combination risk classifier composed of two elevated-risk groups: 1) women with CA125 or HE4 exceeding a 98% specificity threshold; and 2) women with intact fallopian tubes, prior use of menopausal hormone therapy for at least two years, and either a first degree relative with breast or ovarian cancer or a personal history of breast cancer. In the WHI OS population, it classified 13% of women as elevated risk, identifying 30% of ovarian cancers diagnosed up to 7.8 years post-enrollment (Hazard Ratio [HR]=2.6, p<0.001). In the WHI CT validation population, it classified 8% of women as elevated risk, identifying 31% of cancers diagnosed within 7 years of enrollment (HR=4.6, p<0.001).
CA125 and HE4 contributed significantly to a risk prediction classifier combining serum markers with epidemiologic risk factors. The hybrid risk classifier may be useful to identify post-menopausal women who would benefit from timely surgical intervention to prevent epithelial ovarian cancer.
ovarian cancer; risk prediction; CA125; HE4
Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies one must specify the levels of accuracy sought. However, justified target levels are rarely available.
We describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases to cost associated with a positive biomarker test in controls. Guidance is offered on soliciting the cost-benefit ratio. The calculations are based on the longstanding decision theoretic concept of providing a net benefit on average in the population and they rely on some assumptions about uniformity of costs and benefits to those tested.
Calculations are illustrated with three applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancers after mammographic screening; and screening for ovarian cancer.
It is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early phase studies. Nevertheless biomarkers meeting the criteria should still be tested rigorously in studies where the actual impact on patient outcomes of using the biomarker to make clinical decisions is measured.
Accuracy; True Positive Rate; Study Design; Biomarker Performance; Cost
Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.
Given the wide differences in HIV-1 viral load (VL) setpoint across subjects as opposed to fairly stable VL over time within an infected individual, it is important to identify host and viral characteristics that affect VL setpoint. While recently-infected individuals with multiple phylogenetically-linked HIV-1 founder variants represent a minority of HIV-1 infections, we found in two different cohorts that more diverse HIV-1 populations in early infection were associated with significantly higher VL one year after HIV-1 diagnosis.
Biomarkers associated with heterogeneity in subject responses to treatment hold potential for treatment selection. In practice, the decision regarding whether to adopt a treatment-selection marker depends on the effect of using the marker on the rate of targeted disease and on the cost associated with treatment. We propose an expected benefit measure that incorporates both effects to quantify a marker's treatment-selection capacity. This measure builds upon an existing decision-theoretic framework, but is expanded to account for the fact that optimal treatment absent marker information varies with the cost of treatment. In addition, we establish upper and lower bounds on the expected benefit for a perfect treatment-selection rule which provides the basis for a standardized expected benefit measure. We develop model-based estimators for these measures in a randomized trial setting and evaluate their asymptotic properties. An adaptive bootstrap confidence interval is proposed for inference in the presence of non-regularity. Alternative estimators robust to risk model misspecification are also investigated. We illustrate our methods using the Diabetes Control and Complications Trial where we evaluate the expected benefit of baseline hemoglobin A1C in selecting diabetes treatment.
Adaptive bootstrap; Biomarker; Expected benefit; Potential outcomes; Treatment selection
Phase 1 preventive HIV vaccine trials are often designed as randomized, double-blind studies with the inclusion of placebo recipients. Careful consideration is needed to determine when the inclusion of placebo recipients is highly advantageous and when it is optional for achieving the study objectives of assessing vaccine safety, tolerability and immunogenicity. The inclusion of placebo recipients is generally important to form a reference group that ensures fair evaluation and interpretation of subjective study endpoints, or endpoints whose levels may change due to exposures besides vaccination. In some settings, however, placebo recipients are less important because other data sources and tools are available to achieve the study objectives.
randomization; blinding; sample sizes; clinical trial; vaccine safety; vaccine tolerability; vaccine immunogenicity
Three phase 2b, double-blind, placebo-controlled, randomized efficacy trials have tested recombinant Adenovirus serotype-5 (rAd5)-vector preventive HIV-1 vaccines: MRKAd5 HIV-1 gag/pol/nef in Step and Phambili, and DNA/rAd5 HIV-1 env/gag/pol in HVTN505. Due to efficacy futility observed at the first interim analysis in Step and HVTN505, participants of all three studies were unblinded to their vaccination assignments during the study but continued follow–up. Rigorous meta-analysis can provide crucial information to advise the future utility of rAd5-vector vaccines.
We included participant-level data from all three efficacy trials, and three Phase 1–2 trials evaluating the HVTN505 vaccine regimen. We predefined two co-primary analysis cohorts for assessing the vaccine effect on HIV-1 acquisition. The modified-intention-to-treat (MITT) cohort included all randomly assigned participants HIV-1 uninfected at study entry, who received at least the first vaccine/placebo, and the Ad5 cohort included MITT participants who received at least one dose of rAd5-HIV vaccine or rAd5-placebo. Multivariable Cox regression models were used to estimate hazard ratios (HRs) of HIV-1 infection (vaccine vs. placebo) and evaluate HR variation across vaccine regimens, time since vaccination, and subgroups using interaction tests.
Results are similar for the MITT and Ad5 cohorts; we summarize MITT cohort results. Pooled across the efficacy trials, over all follow-up time 403 (n = 224 vaccine; n = 179 placebo) of 6266 MITT participants acquired HIV-1, with a non-significantly higher incidence in vaccine recipients (HR 1.21, 95% CI 0.99–1.48, P = 0.06). The HRs significantly differed by vaccine regimen (interaction P = 0.03; MRKAd5 HR 1.41, 95% CI 1.11–1.78, P = 0.005 vs. DNA/rAd5 HR 0.88, 95% CI 0.61–1.26, P = 0.48). Results were similar when including the Phase 1–2 trials. Exploratory analyses based on the efficacy trials supported that the MRKAd5 vaccine-increased risk was concentrated in Ad5-positive or uncircumcised men early in follow-up, and in Ad5-negative or circumcised men later. Overall, MRKAd5 vaccine-increased risk was evident across subgroups except in circumcised Ad5-negative men (HR 0.97, 95% CI 0.58−1.63, P = 0.91); there was little evidence that the DNA/rAd5 vaccine, that was tested in this subgroup, increased risk (HR 0.88, 95% CI 0.61–1.26, P = 0.48). When restricting the analysis of Step and Phambili to follow-up time before unblinding, 114 (n = 65 vaccine; n = 49 placebo) of 3770 MITT participants acquired HIV-1, with a non-significantly higher incidence in MRKAd5 vaccine recipients (HR 1.30, 95% CI 0.89–1.14, P = 0.18).
Interpretation and Significance
The data support increased risk of HIV-1 infection by MRKAd5 over all follow-up time, but do not support increased risk of HIV-1 infection by DNA/rAd5. This study provides a rationale for including monitoring plans enabling detection of increased susceptibility to infection in HIV-1 at-risk populations.
Markers that predict treatment effect have the potential to improve patient outcomes. For example, the Oncotype DX
® Recurrence Score® has some ability to predict the benefit of adjuvant chemotherapy over and above hormone therapy for the treatment of estrogen-receptor-positive breast cancer, facilitating the provision of chemotherapy to women most likely to benefit from it. Given that the score was originally developed for predicting outcome given hormone therapy alone, it is of interest to develop alternative combinations of the genes comprising the score that are optimized for treatment selection. However most methodology for combining markers is useful when predicting outcome under a single treatment. We propose a method for combining markers for treatment selection which requires modeling the treatment effect as a function of markers. Multiple models of treatment effect are fit iteratively by upweighting or “boosting” subjects potentially misclassified according to treatment benefit at the previous stage. The boosting approach is compared to existing methods in a simulation study based on the change in expected outcome under marker-based treatment. The approach improves upon methods in some settings and has comparable performance in others. Our simulation study also provides insights as to the relative merits of the existing methods. Application of the boosting approach to the breast cancer data, using scaled versions of the original markers, produces marker combinations that may have improved performance for treatment selection.
Biomarker; Boosting; Model mis-specification; Treatment selection
The Net Reclassification Index (NRI) and its P value are used to make conclusions about improvements in prediction performance gained by adding a set of biomarkers to an existing risk prediction model. Although proposed only 5 years ago, the NRI has gained enormous traction in the risk prediction literature. Concerns have recently been raised about the statistical validity of the NRI.
Using a population dataset of 10000 individuals with an event rate of 10.2%, in which four biomarkers have no predictive ability, we repeatedly simulated studies and calculated the chance that the NRI statistic provides a positive statistically significant result. Subjects for training data (n = 420) and test data (n = 420 or 840) were randomly selected from the population, and corresponding NRI statistics and P values were calculated. For comparison, the change in the area under the receiver operating characteristic curve and likelihood ratio statistics were calculated.
We found that rates of false-positive conclusions based on the NRI statistic were unacceptably high, being 63.0% in the training datasets and 18.8% to 34.4% in the test datasets. False-positive conclusions were rare when using the change in the area under the curve and occurred at the expected rate of approximately 5.0% with the likelihood ratio statistic.
Conclusions about biomarker performance that are based primarily on a statistically significant NRI statistic should be treated with skepticism. Use of NRI P values in scientific reporting should be halted.
Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.
Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.
The HIV prevention landscape is evolving rapidly, and future efficacy trials of candidate vaccines, which remain the best long-term option for stemming the HIV epidemic, will be conducted in the context of partially effective nonvaccine prevention modalities. It is essential that these trials provide for valid and efficient evaluation of vaccine efficacy and immune correlates. The availability of partially effective prevention modalities presents opportunities to study their interactions with vaccines to maximally reduce HIV incidence. This article proposes an approach for conducting future vaccine efficacy trials in the context of background use of partially effective nonvaccine prevention modalities, and for conducting future vaccine efficacy trials that provide nonvaccine prevention modalities in one or more of the randomized study groups. Strategies are discussed for responding to emerging evidence on nonvaccine prevention modalities during ongoing vaccine trials. Next-generation HIV vaccine efficacy trials will almost certainly be more complex in their design and implementation but may become more relevant to at-risk populations and better suited to the ultimate goal of reducing HIV incidence at the population level.
The contribution of host T-cell immunity and HLA class I alleles to the control of human immunodeficiency virus (HIV-1) replication in natural infection is widely recognized. We assessed whether vaccine-induced T-cell immunity, or expression of certain HLA alleles, impacted HIV-1 control after infection in the Step MRKAd5/HIV-1 gag/pol/nef study. Vaccine-induced T cells were associated with reduced plasma viremia, with subjects targeting ≥3 gag peptides presenting with half-log lower mean viral loads than subjects without Gag responses. This effect was stronger in participants infected proximal to vaccination and was independent of our observed association of HLA-B*27, –B*57 and –B*58:01 alleles with lower HIV-1 viremia. These findings support the ability of vaccine-induced T-cell responses to influence postinfection outcome and provide a rationale for the generation of T-cell responses by vaccination to reduce viremia if protection from acquisition is not achieved. Clinical trials identifier: NCT00095576.
HIV-1 vaccine; Step study; Gag-specific T cells; HLA class I alleles
Purpose of review
With multiple HIV vaccine candidates suitable for efficacy evaluation in a rapidly changing HIV prevention landscape, innovative HIV vaccine trial design research is much needed to optimally utilize resources by building on lessons learned from past HIV vaccine efficacy trials.
Several recent articles propose new vaccine efficacy trial design strategies tailored to the emerging needs in HIV vaccine evaluation. These include a focus on efficacy evaluation proximal to the vaccination series; more intensive interim monitoring for potential harm, non-efficacy and high efficacy of the vaccine; simultaneous evaluation of multiple vaccine regimens with a shared placebo group; designs that include pilot immunogenicity studies of putative immune correlates to expedite their evaluation; as well as designs tailored to evaluate vaccine efficacy in the context of partially effective non-vaccine prevention modalities.
A more rapid evaluation of multiple vaccine candidates is possible. Weaker vaccines can be weeded out quickly. Pilot studies can be done during the trial to prepare for a timely immune correlates assessment. Evidence that emerges regarding the efficacy of non-vaccine prevention modalities will have important implications for future trial designs.
HIV prevention; multi-arm trial; vaccine efficacy; immune correlates
The phase III RV144 HIV-1 vaccine trial estimated vaccine efficacy (VE) to be 31.2%. This trial demonstrated that the presence of HIV-1–specific IgG-binding Abs to envelope (Env) V1V2 inversely correlated with infection risk, while the presence of Env-specific plasma IgA Abs directly correlated with risk of HIV-1 infection. Moreover, Ab-dependent cellular cytotoxicity responses inversely correlated with risk of infection in vaccine recipients with low IgA; therefore, we hypothesized that vaccine-induced Fc receptor–mediated (FcR-mediated) Ab function is indicative of vaccine protection. We sequenced exons and surrounding areas of FcR-encoding genes and found one FCGR2C tag SNP (rs114945036) that associated with VE against HIV-1 subtype CRF01_AE, with lysine at position 169 (169K) in the V2 loop (CRF01_AE 169K). Individuals carrying CC in this SNP had an estimated VE of 15%, while individuals carrying CT or TT exhibited a VE of 91%. Furthermore, the rs114945036 SNP was highly associated with 3 other FCGR2C SNPs (rs138747765, rs78603008, and rs373013207). Env-specific IgG and IgG3 Abs, IgG avidity, and neutralizing Abs inversely correlated with CRF01_AE 169K HIV-1 infection risk in the CT- or TT-carrying vaccine recipients only. These data suggest a potent role of Fc-γ receptors and Fc-mediated Ab function in conferring protection from transmission risk in the RV144 VE trial.
The RV144 HIV-1 vaccine trial demonstrated partial efficacy of 31% against HIV-1 infection. Studies into possible correlates of protection found that antibodies specific to the V1 and V2 (V1/V2) region of envelope correlated inversely with infection risk and that viruses isolated from trial participants contained genetic signatures of vaccine-induced pressure in the V1/V2 region. We explored the hypothesis that the genetic signatures in V1 and V2 could be partly attributed to selection by vaccine-primed T cells. We performed a T-cell-based sieve analysis of breakthrough viruses in the RV144 trial and found evidence of predicted HLA binding escape that was greater in vaccine versus placebo recipients. The predicted escape depended on class I HLA A*02- and A*11-restricted epitopes in the MN strain rgp120 vaccine immunogen. Though we hypothesized that this was indicative of postacquisition selection pressure, we also found that vaccine efficacy (VE) was greater in A*02-positive (A*02+) participants than in A*02− participants (VE = 54% versus 3%, P = 0.05). Vaccine efficacy against viruses with a lysine residue at site 169, important to antibody binding and implicated in vaccine-induced immune pressure, was also greater in A*02+ participants (VE = 74% versus 15%, P = 0.02). Additionally, a reanalysis of vaccine-induced immune responses that focused on those that were shown to correlate with infection risk suggested that the humoral responses may have differed in A*02+ participants. These exploratory and hypothesis-generating analyses indicate there may be an association between a class I HLA allele and vaccine efficacy, highlighting the importance of considering HLA alleles and host immune genetics in HIV vaccine trials.
IMPORTANCE The RV144 trial was the first to show efficacy against HIV-1 infection. Subsequently, much effort has been directed toward understanding the mechanisms of protection. Here, we conducted a T-cell-based sieve analysis, which compared the genetic sequences of viruses isolated from infected vaccine and placebo recipients. Though we hypothesized that the observed sieve effect indicated postacquisition T-cell selection, we also found that vaccine efficacy was greater for participants who expressed HLA A*02, an allele implicated in the sieve analysis. Though HLA alleles have been associated with disease progression and viral load in HIV-1 infection, these data are the first to suggest the association of a class I HLA allele and vaccine efficacy. While these statistical analyses do not provide mechanistic evidence of protection in RV144, they generate testable hypotheses for the HIV vaccine community and they highlight the importance of assessing the impact of host immune genetics in vaccine-induced immunity and protection. (This study has been registered at ClinicalTrials.gov under registration no. NCT00223080.)
A safe and effective vaccine for the prevention of human immunodeficiency virus type 1 (HIV-1) infection is a global priority. We tested the efficacy of a DNA prime–recombinant adenovirus type 5 boost (DNA/rAd5) vaccine regimen in persons at increased risk for HIV-1 infection in the United States.
At 21 sites, we randomly assigned 2504 men or transgender women who have sex with men to receive the DNA/rAd5 vaccine (1253 participants) or placebo (1251 participants). We assessed HIV-1 acquisition from week 28 through month 24 (termed week 28+ infection), viral-load set point (mean plasma HIV-1 RNA level 10 to 20 weeks after diagnosis), and safety. The 6-plasmid DNA vaccine (expressing clade B Gag, Pol, and Nef and Env proteins from clades A, B, and C) was administered at weeks 0, 4, and 8. The rAd5 vector boost (expressing clade B Gag-Pol fusion protein and Env glycoproteins from clades A, B, and C) was administered at week 24.
In April 2013, the data and safety monitoring board recommended halting vaccinations for lack of efficacy. The primary analysis showed that week 28+ infection had been diagnosed in 27 participants in the vaccine group and 21 in the placebo group (vaccine efficacy, −25.0%; 95% confidence interval, −121.2 to 29.3; P = 0.44), with mean viral-load set points of 4.46 and 4.47 HIV-1 RNA log10 copies per milliliter, respectively. Analysis of all infections during the study period (41 in the vaccine group and 31 in the placebo group) also showed lack of vaccine efficacy (P = 0.28). The vaccine regimen had an acceptable side-effect profile.
The DNA/rAd5 vaccine regimen did not reduce either the rate of HIV-1 acquisition or the viral-load set point in the population studied. (Funded by the National Institute of Allergy and Infectious Diseases; ClinicalTrials.gov number, NCT00865566.)
The HIV epidemic has carved contrasting trajectories around the world with sub-Saharan Africa (SSA) being most affected. We hypothesized that mean HIV-1 plasma RNA viral loads (VL) are higher in SSA than other areas, and that these elevated levels may contribute to the scale of epidemics in this region.
Design and Methods
To evaluate this hypothesis, we constructed a database of means of 71,668 VL measurements from 44 cohorts in seven regions of the world. We used linear regression statistical models to estimate differences in VL between regions. We also constructed and analyzed a mathematical model to describe the impact of the regional VL differences on HIV epidemic trajectory.
We found substantial regional VL heterogeneity. The mean VL in SSA was 0.58 log10 copies/mL higher than in North America (95% CI: 0.45 to 0.71); this represents about a 4-fold increase. The highest mean VLs were found in Southern and East Africa, while in Asia, Europe, North America, and South America, mean VLs were comparable. Mathematical modeling indicated that conservatively 14% of HIV infections in a representative population in Kenya could be attributed to the enhanced infectiousness of subjects with heightened VL.
We conclude that community VL appears to be higher in SSA than in other regions and this may be a central driver of the massive HIV epidemics in this region. The elevated VLs in SSA may reflect, among other factors, the high burden of co-infections or the preponderance of HIV-1 subtype C infection.
HIV; viral load; co-infection; epidemic; sub-Saharan Africa; mathematical model
In the RV144 trial, the estimated efficacy of a vaccine regimen against human immunodeficiency virus type 1 (HIV-1) was 31.2%. We performed a case–control analysis to identify antibody and cellular immune correlates of infection risk.
In pilot studies conducted with RV144 blood samples, 17 antibody or cellular assays met prespecified criteria, of which 6 were chosen for primary analysis to determine the roles of T-cell, IgG antibody, and IgA antibody responses in the modulation of infection risk. Assays were performed on samples from 41 vaccinees who became infected and 205 uninfected vaccinees, obtained 2 weeks after final immunization, to evaluate whether immune-response variables predicted HIV-1 infection through 42 months of follow-up.
Of six primary variables, two correlated significantly with infection risk: the binding of IgG antibodies to variable regions 1 and 2 (V1V2) of HIV-1 envelope proteins (Env) correlated inversely with the rate of HIV-1 infection (estimated odds ratio, 0.57 per 1-SD increase; P = 0.02; q = 0.08), and the binding of plasma IgA antibodies to Env correlated directly with the rate of infection (estimated odds ratio, 1.54 per 1-SD increase; P = 0.03; q = 0.08). Neither low levels of V1V2 antibodies nor high levels of Env-specific IgA antibodies were associated with higher rates of infection than were found in the placebo group. Secondary analyses suggested that Env-specific IgA antibodies may mitigate the effects of potentially protective antibodies.
This immune-correlates study generated the hypotheses that V1V2 antibodies may have contributed to protection against HIV-1 infection, whereas high levels of Env-specific IgA antibodies may have mitigated the effects of protective antibodies. Vaccines that are designed to induce higher levels of V1V2 antibodies and lower levels of Env-specific IgA antibodies than are induced by the RV144 vaccine may have improved efficacy against HIV-1 infection.
Treatment-selection markers are biological molecules or patient characteristics associated with one’s response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a marker’s capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not suffcient for good marker performance. In this paper, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the marker’s performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of pre-existing immunity to Adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition.
Classification accuracy; Constrained maximum likelihood; Monotone treatment effect; Potential outcomes; Sensitivity analysis; Treatment-selection marker
Extensive observational data suggest that HSV-2 infection may
facilitate HIV acquisition, increase HIV viral load, and accelerate HIV
progression and onward transmission. To explore these relationships, we
examined the impact of pre-existing HSV-2 infection in an international HIV
We analyzed the associations between prevalent HSV-2 infection and
HIV-1 acquisition and progression among 1836 men who have sex with men
(MSM). We used Cox proportional hazards regression models to estimate the
association between HSV-2 infection and both HIV acquisition and ART
initiation, and linear regression to explore the effect of HSV-2 on pre-ART
HSV-2 infection increased risk of HIV-1 acquisition among all
volunteers (adjusted hazard ratio 2.2; 95% CI, 1.4 to 3.5).
Adjusting for demographic variables, circumcision, Ad5 titer and significant
risk behaviors, the risk of HIV acquisition among HSV-2 infected placebo
recipients was three fold higher than HSV-2 seronegatives (hazard ratio 3.3;
95% CI, 1.6 to 6.9). Past HSV-2 infection was associated with a 0.2
log10 copies/ml higher adjusted mean set point viral load
(95% CI, 0.3 lower to 0.6 higher). HSV-2 infection was not
associated with time to ART initiation.
Among MSM in an HIV-1 vaccine trial, pre-existing HSV-2 infection was
a major risk factor for HIV acquisition. Past HSV-2 did not significantly
increase HIV viral load or early disease progression. HSV-2 seropositive
persons will likely prove more difficult than HSV-2 seronegative persons to
protect against HIV infection using vaccines or other prevention
Herpes Simplex Virus Type II; HIV incidence