Background: Some candidate HIV-1 vaccines may not prevent HIV-1 infection but may alter the course of disease. Surrogate endpoints based on early laboratory makers in HIV-1–infected persons who are antiretroviral therapy (ART)–naive will be useful for evaluating vaccine efficacy in slowing disease progression (VEp). We examined pretreatment HIV-1 viral loads and CD4 cell counts in recent HIV-1 seroconverters to inform selection of these endpoints.
Methods: We studied 130 newly HIV-1–infected injection drug users identified from a prospective cohort of initially uninfected persons in Bangkok during 1995 through 1998. We analyzed trends in HIV-1 viral loads and CD4 cell counts as well as progression to the surrogate endpoint, defined as 2 consecutive CD4 cell counts of fewer than 350 cells/mm3, during 24 months after the first HIV-1 seropositive (FP) visit.
Results: Median HIV-1 RNA copies/mL with interquartile ranges were 43,693 (14,320–94,767) at the FP visit, 46,924 (16,273–104,314) at 6 months, 28,446 (11,292–54,325) at 12 months, and 18,080 (8713–54,059) at 18 months. HIV-1 viral loads at the FP visit and at 18 months were positively correlated (r = 0.53, P < 0.0001). Of 130 participants, 12% reached the surrogate endpoint by 6 months, 16% by 12 months, and 27% by 18 months. In Cox regression analyses, HIV-1 viral loads of more than 50,000 copies/mL at the FP visit (hazard ratio [HR] = 2.3, 95% confidence interval [CI]: 1.1–4.8) and first CD4 cell count of 500 or fewer cells/mm3 (HR = 7.6, 95% CI: 3.2–17.6) were independently associated with faster progression to the surrogate endpoint.
Conclusions: Participants with high HIV-1 RNA levels and low CD4 cell counts close to the time of seroconversion were more likely to experience early immunologic progression. Approximately one quarter of seroconverters reached the surrogate immunologic endpoint within 18 months of their FP visit and before starting ART, suggesting the utility of this endpoint for analyses of VEp in some ongoing and planned HIV-1 vaccine efficacy trials.
From the *Epidemic Intelligence Service, Division of Applied Public Health Training, Epidemiology Program Office, Centers for Disease Control and Prevention, Atlanta, GA; †Division of HIV/AIDS Prevention, National Center for HIV, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA; ‡Bangkok Metropolitan Administration, Bangkok, Thailand; §Thailand Ministry of Public Health–US Centers for Disease Control and Prevention Collaboration, Nonthaburi, Thailand; and ‖Mahidol University, Bangkok, Thailand.
Received for publication August 5, 2003; accepted January 5, 2004.
Financially supported by the US Centers for Disease Control and Prevention as part of a research collaboration with the Bangkok Metropolitan Administration and the Ministry of Public Health of Thailand.
Reprints: Kate Buchacz, Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Mailstop E-45, Atlanta, GA 30333 (e-mail: email@example.com).
Because of numerous challenges in the development of a vaccine against HIV-1, 1 the first-generation vaccines available for public health use may not prevent HIV-1 infection, 2,3 Some current HIV-1 vaccine candidates, however, may reduce disease progression in vaccinated individuals who subsequently become infected by lowering HIV-1 viral load during and after primary infection, as illustrated by studies of non-human primates. 4,5 Consequently, the current and future phase 3 trials of HIV-1 vaccines must assess both vaccine efficacy in reducing susceptibility to HIV-1 infection and vaccine efficacy in slowing HIV-1 disease progression (VEp) by comparing markers such as CD4 cell counts and HIV-1 RNA levels of vaccinees and controls who become HIV infected during the trial. 6,7
HIV-1 RNA levels and CD4 cell counts in early and established infection are strong and independent predictors of clinical HIV-1 disease progression and mortality. 8–10 Surrogate endpoints for clinical outcomes based on threshold levels or changes in HIV-1 RNA and CD4 cell counts have been used to support the licensure of antiretroviral medications. 11 These surrogate end points will also be valuable for evaluating the efficacy of HIV-1 vaccines in slowing disease progression. 12,13 In the era of effective antiretroviral therapy (ART), it is neither ethical nor feasible to study the effects of a vaccine on the long-term progression of HIV-1 disease in the absence of treatment; the analyses of VEp thus must focus on early immunologic and virologic events before ART is initiated. The use of early progression endpoints allows a preliminary assessment of vaccine efficacy on disease progression during a relatively short follow-up of HIV-infected trial participants. Longer term follow-up, however, remains important to elucidate the extent to which the potential effects on surrogate endpoints parallel the effects on clinical endpoints.
The HIV-1 epidemic among injection drug users (IDUs) in Bangkok has been well characterized for more than a decade. 14 After extensive vaccine preparatory work in the Bangkok Metropolitan Administration (BMA) cohort of IDUs during 1995 through 1998, 15 a phase 3 HIV-1 vaccine trial was launched in 1999. 16,17 The continued follow-up of seroconverters from the BMA vaccine preparatory cohort provides an opportunity to examine the levels and variability in early CD4 T-cell counts and HIV-1 viral loads 18 before the use of ART, which can guide sample size requirements for analyses of continuous VEp endpoints. It also allows us to investigate early HIV-1 disease progression and to explore the frequencies of some categorical immunologic endpoints for evaluating VEp. Finally, studying risk factors for early disease progression in this cohort can help us to identify potential confounders to adjust for when assessing VEp. The key objectives of our analyses were to (1) describe the immunologic and virologic profiles during the first 24 months after HIV-1 seroconversion, (2) evaluate the relationship between the initial HIV-1 RNA viral load and CD4 cell counts and later immunologic deterioration, and (3) examine the role of HIV-1 subtype in HIV-1 disease progression.
The BMA manages a large municipal drug treatment program in Bangkok, Thailand, where clients receive methadone, medical care, and information about HIV-1 prevention. As described previously, 15,18 by screening participants for HIV-1 antibodies every 4 months, 130 seroconverters were identified from a vaccine preparatory cohort of IDUs during 1995 through 1998. Following a positive HIV-1 antibody test result, these persons were offered, with voluntary informed consent, enrollment in a prospective study of HIV-infected persons. Blood specimens were collected as soon as possible after the first HIV-1–seropositive visit (FP), 1 month later, and every 4 months thereafter. This study protocol was approved by the Research and Human Subjects Review Committee, Ministry of Public Health, Nonthaburi, Thailand, and by the Institutional Review Board, Centers for Disease Control and Prevention, Atlanta, Georgia.
At each study visit, participants were evaluated clinically and referred for treatment according to BMA guidelines for the treatment of HIV infection. 15 In 1999, 2 antiretroviral drugs were offered after a person had 2 CD4 cell counts of fewer than 500 cells/mm3; starting in October 2001, 3 antiretroviral drugs and prophylaxis for opportunistic infections were offered after a person had 1 CD4 cell count of fewer than 200 cells/mm3.
Blood specimens from the last seronegative visit, the FP visit, and all later visits were tested to determine HIV-1 RNA levels by the Amplicor HIV-1 Monitor Test, version 1.5 (Roche Diagnostics, Branchburg, NJ). The lower limit of quantitation was 400 RNA copies/mL. CD4 cell counts were determined by FACScan cytometry.
Genetic sequencing and phylogenetic analyses had revealed that 103 (79%) study participants were infected with subtype CRF01_AE (a circulating recombinant form AE, referred to here as subtype E) and 27 (21%) were infected with subtype B. 19 IDUs infected with subtypes B and E were similar in terms of sociodemographic and behavioral characteristics. 18 Although IDUs infected with subtype E had significantly higher RNA HIV-1 levels during the first 6 months after seroconversion than did IDUs infected with subtype B, these differences diminished over time. 18
The seroconversion interval was defined as the time between the dates of the last HIV-1–seronegative visit and the FP visit. The date of seroconversion was estimated as the midpoint of the seroconversion interval. The analysis data set included all available observations up to 24 months after the FP visit, except for observations on 6 persons who received ART during this time; data on these persons were right-censored at the last pre-ART visit.
HIV-1 RNA levels were summarized at the FP visit and at visits closest to 6, 12, and 18 months (±3 months) after the FP visit. Nine HIV-1 RNA measurements below the limit of detection (<400 copies/mL) were set at 200 copies/mL for purposes of analysis. CD4 cell counts were first assessed at the second study visit, which was scheduled as soon as possible after the FP visit. CD4 cell counts were thus summarized as first available within 3 months of the FP visit and at visits closest to 6, 12, and 18 months (±3 months) after the FP visit.
Trajectories of HIV-1 RNA and CD4 cell counts were described using the lowess procedure, which draws a representative smooth curve through data using robust local regressions. 20 Trends in CD4 cell counts and log10 HIV-1 RNA levels 6 to 24 months after the FP visit were analyzed using mixed-effects regression models that adjusted for within-person correlations of measurements. 21 These models provide estimates of average linear marker trajectories over time. We also estimated the mean loss in CD4 cell count during the first 6, 12, and 18 months from the FP visit to account for the decreasing rate of CD4 cell loss over time.
We defined the endpoint for early immunologic disease progression as 2 consecutive CD4 cell counts of fewer than 350 cells/mm3 (to discount early transient drops in CD4 cell counts in primary infection). We chose this endpoint for our analyses of pretreatment HIV-1 disease progression because of supporting epidemiologic data and current recommendations for initiating ART when HIV-infected persons have CD4 cell counts of 200 to 350 cells/mm3.22–25 We also chose this endpoint because few HIV-1–infected persons may be expected to reach CD4 cell counts of fewer than 200 cells/mm3 in the time frame of a standard phase 3 vaccine trial. We used Cox proportional hazard models and Kaplan-Meier estimates with log-rank tests 26 to examine progression to the endpoint by the HIV-1 viral load at the FP visit and the first available CD4 cell count (referred to here as “baseline” predictors). In defining baseline predictor variables, we chose CD4 cell count strata of ≤500 and >500 cells/mm3 and HIV-1 RNA strata of ≤50,000 and >50,000 copies/mL because these cutoff levels are clinically meaningful for prognosis of HIV disease progression, 10,25 and each divided our relatively small population of 130 seroconverters into 2 well-balanced subgroups. We did not assess the predictive value of HIV-1 viral load at 6 months or later, because a substantial number of participants had already reached the immunologic endpoint by 6 months after the FP visit. We assessed whether risk factors were similar for progression to other immunologic endpoints. We used graphics methods to verify that proportional hazards assumptions were not violated. All analyses were performed using Statistical Analysis Software (SAS), version 8.02 (SAS Institute, Cary, NC).
Of the 130 IDUs who seroconverted, 116 (89%) were men, and 14 (11%) were women; 103 were infected with HIV-1 subtype E, and 27 were infected with subtype B. The mean age at enrollment was 29.8 years. The median seroconversion interval was 4.1 months (interquartile range [IQR]: 3.9–4.4). Six IDUs died within 24 months of the FP visit with cause of death recorded as AIDS (n =1), pneumonia (n =1), lung and kidney failure (n = 1), overdose (n =2), and murder (n = 1). By 6, 12, and 18 months after the FP visit, 118, 113, and 107 participants, respectively, remained in follow-up and continued to contribute CD4 cell count and HIV-1 viral load data. A total of 847 HIV-1 RNA values and 709 CD4 cell counts were available for analysis.
Median HIV-1 RNA was 43,694 (mean = 129,648) copies/mL at the FP visit (Table 1). After 1 outlying viral load of more than 6 million copies/mL was excluded, mean HIV-1 RNA at the FP visit was 81,009 copies/mL (SD = 122,477). In cross-sectional analyses of all viral loads available at landmark time points (see Table 1), 46% of participants had HIV-1 RNA of more than 50,000 copies/mL at their FP visit; that proportion diminished to 27% at 18 months. HIV-1 RNA levels at the FP visit were positively correlated with RNA levels at 18 months after the FP visit (Spearman r = 0.53, P < 0.0001). The median (mean) peak HIV-1 RNA levels within 3 months of the FP visit were 79,997 (mean = 236,604) copies/mL; in 63% of participants HIV-1 RNA peaked after the FP visit. Only 4 participants had HIV-1 RNA levels below the limit of detection during the 24 months. After initial increases, the population-averaged HIV-1 viral loads fell to stable levels, but interindividual variability in viral loads was high throughout the 24 months after seroconversion (Fig. 1). Using longitudinal regression analyses, we estimated a mean change in HIV-1 viral load of −0.015 log10 RNA copies/mL between 6 and 24 months after the FP visit (P = 0.0004).
The 115 participants whose first measurement was within 3 months of the FP visit had a median CD4 cell count of 540 cells/mm3 (see Table 1). Of these persons, 19 (17%) had a first CD4 cell count of fewer than 350 cells/mm3 and 12 (10%) maintained a CD4 cell count of fewer than 350 cells/mm3 up to 24 months. On average, CD4 cell counts decreased most rapidly during the first 6 months after seroconversion and at a slower rate thereafter (Fig. 2). In the longitudinal regression analyses, CD4 cell counts were estimated to decline at a mean rate of −3.0 cells/mm3 per month between 6 and 24 months after the FP visit. The mean decline in CD4 cell count was estimated at −77 cells/mm3 from the FP visit to 6 months and at −112 cells/mm3 from the FP visit to 18 months (Table 2). Although participants whose HIV-1 RNA was more than 50,000 copies/mL at the FP visit had lower initial (intercept) CD4 cell counts than did those with HIV-1 RNA of 50,000 or fewer copies/mL (520 vs. 611 cells/mm3; P = 0.04), we found no differences in the rates of CD4 cell loss according to HIV-1 RNA level at the FP visit (see Table 2). Participants with lower baseline CD4 cell counts experienced slower loss or transient increases in CD4 cell counts (see Table 2).
Kaplan-Meier estimates of the proportion of participants reaching the immunologic endpoint (2 consecutive CD4 cell counts of fewer than 350 cells/mm3) were 12% by 6 months, 16% by 12 months, and 27% by 18 months. First RNA of more than 50,000 copies/mL was associated with faster progression to the endpoint (P = 0.002;Fig. 3), as was first CD4 cell count of 500 or fewer cells/mm3 (P = 0.0001;Fig. 4). Progression rates did not differ by HIV-1 subtype (P = 0.65). In the univariate Cox regression models, the hazard of progression to the immunologic endpoint was higher for participants whose baseline CD4 cell count was 500 or fewer cells/mm3 or for those whose HIV-1 RNA level was more than 50,000 copies/mL (Table 3). The risk of disease progression, however, was not associated with HIV-1 viral subtype, participant's age or gender, calendar year of the FP visit (see Table 3), or frequency of injection or needle sharing (data not shown). In the multivariate analyses, higher baseline HIV-1 RNA level and lower CD4 cell count remained independently associated with progression to the immunologic endpoint (see Table 3).
None of the results changed appreciably after we excluded 7 persons with seroconversion intervals of more than 12 months or after we excluded 5 persons whose first CD4 count was measured 4 months or more after the FP visit. We also explored the consistency of risk factor associations for progression to 1 CD4 cell count of fewer than 250 cells/mm3. We chose this alternate endpoint because 27 participants reached a CD4 cell count of fewer than 250 cells/mm3 during the 24 months after the FP visit, whereas only 12 participants reached 1 CD4 cell count of fewer than 200 cells/mm3. Analyses of baseline markers for progression to 1 CD4 cell count of fewer than 250 cells/mm3 revealed similar associations: adjusted hazard ratio (HR) of 8.7 (95% confidence interval [CI]: 3.0–25.5) for CD4 counts of fewer than 500 cells/mm3 and adjusted HR of 2.1 (CI: 0.9–4.8) for HIV-1 RNA of more than 50,000 copies/mL.
In this well-characterized cohort of HIV-1 seroconverters, high HIV-1 viral load and low CD4 cell counts close to the time of seroconversion were predictive of progression to the early immunologic endpoint, defined as 2 consecutive CD4 cell counts of fewer than 350 cells/mm3. A substantial proportion (27%) of participants reached the immunologic endpoint by 18 months after the FP visit. These results and the data on pretreatment levels and variability of HIV-1 viral load and CD4 cell counts in Table 1 provide a useful reference for determining sample sizes and for modeling both categorical and continuous outcomes in the analyses of HIV-1 vaccine efficacy on disease progression in the ongoing and future phase 3 vaccine trials.
Although HIV-1 disease progression has been previously examined in cohorts of recently HIV-1–infected IDUs, 27–29 the particular strengths of our study are that CD4 cell counts and HIV-1 RNA levels were measured frequently and that the first measurements were performed soon after seroconversion. The median viral load of 43,694 copies/mL at the FP visit (a median of 2 months after the estimated date of seroconversion) in Thai IDUs was similar to that reported for male IDUs in the United States (50,766 copies/mL at a median of 4.3 months after the estimated date of seroconversion). 29 The median HIV-1 RNA levels and CD4 cell counts during the first year after seroconversion in our study population were also similar to those in other studies of recently HIV-infected persons, predominantly homosexual men, with short seroconversion intervals. 30,31 Compared with participants with well-defined times of HIV-1 seroconversion enrolled in the Multi-center AIDS Cohort Study in the United States, 32 the French SEROCO cohort, 33 and the HIV Network for Prevention Trials, 34 however, the participants in our study had somewhat higher RNA levels and lower CD4 cell counts during the first year after the FP visit, but they had similar rates of CD4 cell count decline. Some differences in early laboratory marker values between studies may be caused by differences in the length of seroconversion intervals or by variability in laboratory assays. Furthermore, HIV-1 RNA levels, CD4 cell counts, and the rate of HIV-1 disease progression may be affected by other factors, including age, gender, race/ethnicity, comorbidities and coinfections (including those related to injection drug use), host immune factors, and viral characteristics, as previously described. 27,28,35–43
Higher HIV-1 viral loads around the time of seroconversion and in the first several months of infection are prognostic of clinical HIV-1 disease progression 9,32,33,44 and, based on our study, appear to have prognostic value for early HIV disease progression as well. Although we detected no differences in the rate of CD4 cell decline by HIV-1 RNA level at the FP visit, we found a moderate association between higher RNA level at the FP visit and progression to the immunologic endpoint stringently defined as 2 consecutive CD4 cell counts of fewer than 350 cells/mm3. As in other studies, 28,31 IDUs in our study who had low CD4 cell counts shortly after seroconversion were at an increased risk for disease progression. Of note, there were no substantial statistically significant differences in participants' seroconversion intervals according to the RNA level at the FP visit (≤50,000 vs. >50,000 copies/mL) or CD4 cell count closest to the FP visit (≤500 vs. >500 cells/mm3), suggesting that differences in the risk of early disease progression for participants in these subgroups did not result from confounding by duration of HIV infection.
Although some new candidate HIV-1 vaccines may reduce HIV-1 viral loads during early infection 4 and may alter the trajectory of CD4 cell counts, it remains unclear whether changes in laboratory marker values close to the time of seroconversion will reliably predict longer term vaccine efficacy on clinical HIV-1 disease progression. Studies evaluating the utility of laboratory markers as surrogate endpoints in ART trials have found that treatment-mediated changes in early CD4 cell counts and HIV-1 RNA levels are imperfect surrogates for clinical disease progression because they do not fully reflect the changes in the risk for AIDS or death associated with use of different ART regimens. 45–47 In addition, the vaccine-induced suppression of early HIV-1 viral load and the preservation of CD4 cell counts may not have the same long-term clinical benefit as natural control of early HIV-1 infection by some persons. Finally, findings of vaccine efficacy on disease progression based on the differences in the immunologic or virologic profiles of the HIV-1–infected vaccine and placebo recipients must be interpreted cautiously because of the potential for selection bias resulting from the fact that these analyses only include a subset of the original randomized population. For example, VEp may be underestimated if a candidate vaccine disproportionately protects persons with stronger immune function such that vaccine recipients who do become HIV-1 infected are also the ones more likely to experience rapid immunologic decline. 48
Several considerations should guide the choice of surrogate endpoints based on either HIV-1 viral load or CD4 cell counts for analyses of VEp. First, whereas HIV-1 RNA levels near time of seroconversion reflect the ability of the immune system to control the infection, declining CD4 cell counts are indicative of cumulative immunologic deterioration and therefore may be more appropriate than HIV-1 RNA levels as a measure of disease progression. Second, it is important to choose secondary endpoints that occur relatively soon after the infection and before patients begin taking ART. One of our key findings is that 27% of participants reached 2 consecutive CD4 cell counts of fewer than 350 cells/mm3 by 18 months after the
FP visit. This endpoint seems to be relevant and evaluable for analyses of VEp in future HIV-1 vaccine trials, particularly in Thailand and other developing countries, where HIV-infected persons are likely to remain ART naive until their CD4 cell counts decline to 350 or fewer cells/mm3. Our study also suggests that virologic “failure” endpoints are common in the first few months after seroconversion. For example, at 6 months after the FP visit, close to half of our participants had an HIV-1 viral load of more than 50 000 copies/mL and approximately one quarter had a viral load of more than 100 000 copies/mL (see Table 1). Third, data on variability in HIV-1 viral load and CD4 cell counts at various points in time (see Table 1) can be useful for estimating the sample sizes necessary for well-powered analyses of VEp based on continuous outcomes. For a vaccine trial of a given size, selecting an endpoint with a smaller variance will make it more likely to detect a difference (if it exists) in mean or median laboratory marker levels of HIV-infected vaccinees and placebo recipients.
We limited our analyses to the first 24 months after the FP visit, because the initial evaluations of VEp in an HIV-1 vaccine trial will likely be based on similar or shorter follow-up of HIV-infected trial participants (before they begin ART). As was the case in our cohort, few persons who become infected in an HIV-1 vaccine trial may be expected to progress to AIDS or death during this time period. Therefore, our analyses focused on progression to the immunologic endpoint of 2 consecutive CD4 cell counts of fewer than 350 cells/mm3, which was reached by a larger number of participants (n = 36) during the first 24 months after the FP visit than was the endpoint of 1 CD4 cell count of fewer than 250 cells/mm3 (n = 27) or 1 CD4 cell count of fewer than 200 cells/mm3 (n = 12). We could not well assess the risk for progression to this immunologic endpoint by HIV-1 viral load levels at 6 or 12 months after the FP visit, because a substantial number of participants had already reached the immunologic endpoint by that time (12% by 6 months and 16% by 12 months), and excluding these “fast progressors” from analysis could have introduced bias.
Evaluation of VEp in future HIV-1 vaccine trials will be challenging, because some participants will likely begin ART soon after infection, which can dramatically alter the course of HIV-1 disease, 11 whereas those who delay or are not eligible to receive ART may be a select subset of healthier persons. Gilbert et al 12 proposed to use aggregate endpoints combining virologic or immunologic failure and time to initiation of ART. This method can be particularly useful in settings where standardized guidelines for ART initiation are followed. Because only 6 of our participants received ART during the 24 months after the FP visit and the BMA treatment guidelines have continued to evolve since the inception of this cohort, we did not incorporate time to ART use in our analyses.
It is important that future HIV-1 vaccine trials evaluate VEp, because an HIV-1 vaccine that mitigates HIV-1 disease and controls HIV-1 viral load may have important public benefits worldwide in terms of improving and prolonging the lives of those who become HIV-1 infected and potentially reducing the risk of HIV transmission by these vaccinated HIV-1–infected individuals. 49–53 The BMA seroconverter cohort provides useful reference data, including the magnitude and variability in early CD4 cell counts and HIV-1 RNA levels, for treatment-naive recently HIV-1–infected IDUs. These data can guide the analyses of vaccine efficacy in slowing disease progression in planned HIV-1 vaccine efficacy trials in Thailand and elsewhere. 54
The authors are grateful to the participants of the study and the staff of the BMA, Thailand Ministry of Public Health–US Centers for Disease Control and Prevention (MOPH–US CDC) Collaboration, Mahidol University, and the Centers for Disease Control and Prevention for administrative, clinical, laboratory, and data management support. Specifically, they acknowledge the study coordinators from the BMA and the Thailand MOPH–US CDC Collaboration. In addition, the authors thank Michael Martin and Peter Gilbert for comments on earlier drafts of the manuscript and Bob Byers and Peter Gilbert for consultation on statistical issues.
1. Wagner R, Shao Y, Wolf H. Correlates of protection, antigen delivery and molecular epidemiology: basics for designing an HIV vaccine. Vaccine
2. Cohen J. AIDS vaccine trial produces disappointment and confusion. Science
3. McCarthy M. AIDS vaccine fails in Thai trial. Lancet
4. Amara RR, Villinger F, Altman JD, et al. Control of a mucosal challenge and prevention of AIDS by a multiprotein DNA/MVA vaccine. Science
5. Barouch DH, Santra S, Schmitz JE, et al. Control of viremia and prevention of clinical AIDS in rhesus monkeys by cytokine-augmented DNA vaccination. Science
6. Rida W, Fast P, Hoff R, et al. Intermediate-size trials for the evaluation of HIV vaccine candidates: a workshop summary. J Acquir Immune Defic Syndr Hum Retrovirol
7. Hu DJ, Vitek CR, Bartholow B, Mastro TD. Key issues for a potential human immunodeficiency virus vaccine. Clin Infect Dis
8. Margolick JB, Farzadegan H, Hoover DR, et al. Relationship between infectious cell-associated human immunodeficiency virus type 1 load, T lymphocyte subsets, and stage of infection in homosexual men. J Infect Dis
9. Katzenstein TL, Pedersen C, Nielsen C, et al. Longitudinal serum HIV RNA quantification: correlation to viral phenotype at seroconversion and clinical outcome. AIDS
10. Mellors JW, Muñoz A, Giorgi JV, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med
11. Hammer SM, Squires KE, Hughes MD, et al. A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimeter or less. N Engl J Med
12. Gilbert PB, DeGruttola V, Hudgens MG, et al. What constitutes efficacy for a human immunodeficiency virus vaccine that ameliorates viremia: issues involving surrogate endpoints in phase 3 trials. J Infect Dis
13. Gilbert PB, DeGruttola V, Hammer SM, et al. Virologic and regimen termination surrogate endpoints in AIDS clinical trials. JAMA
14. Kitayaporn D, Uneklabh C, Weniger BG, et al. HIV-1 incidence determined retrospectively among drug users in Bangkok, Thailand. AIDS
15. Vanichseni S, Kitayaporn D, Mastro TD, et al. Continued high HIV-1 incidence in a vaccine trial preparatory cohort of injection drug users in Bangkok. Thailand. AIDS
16. Francis DP, Heyward WL, Popovic V, et al. Candidate HIV/AIDS vaccines: lessons learned from the world's first phase III efficacy trials. AIDS
17. Vanichseni S, Tappero JW, Pitisuttitham P, et al. Recruitment, screening and baseline epidemiologic characteristics of injection drug users enrolled in the AIDSVAX® B/E HIV vaccine trial in Bangkok, Thailand: Comparison of characteristics with a preparatory cohort study. AIDS
. (In press)
18. Hu DJ, Vanichseni S, Mastro TD, et al. Viral load differences in early infection with two HIV-1 subtypes. AIDS
19. Subbarao S, Vanichseni S, Hu DJ, et al. Genetic characterization of incident HIV-1 subtype E and B strains from a prospective cohort of injecting drug users in Bangkok, Thailand. AIDS Res Hum Retroviruses
20. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc
21. Diggle PJ, Liang K, Zeger SL. Analysis of Longitudinal Data
. Oxford: Clarendon Press; 1994.
22. Sterling TR, Chaisson RE, Moore RD. HIV-1 RNA, CD4 T-lymphocytes, and clinical response to highly active antiretroviral therapy. AIDS
23. Egger M, May M, Chene G, et al. Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet
24. Opravil M, Ledergerber B, Furrer H, et al. Clinical efficacy of early initiation of HAART in patients with asymptomatic HIV infection and CD4 cell count >350 × 106
25. Centers for Disease Control and Prevention. Guidelines for using antiretroviral agents among HIV-infected adults and adolescents: recommendations of the Panel on Clinical Practices for Treatment of HIV. MMWR Morb Mortal Wkly Rep
. 2002;51(No. RR-7):1–55.
26. Alliston PD. Survival Analyses Using the SAS System: A Practical Guide
. Cary, NC: SAS Publications; 1995.
27. Brettle RP, McNeil AJ, Burns S, et al. Progression of HIV: follow-up of Edinburgh injecting drug users with narrow seroconversion intervals in 1983–1985. AIDS
28. The Italian Seroconversion Study. Disease progression and early predictors of AIDS in HIV-seroconverted injecting drug users. AIDS
29. Sterling TR, Vlahov D, Astemborski J, et al. Initial plasma HIV-1 RNA levels and progression to AIDS in women and men. N Engl J Med
30. Schacker TW, Hughes JP, Shea T, et al. Biological and virologic characteristics of primary HIV infection. Ann Intern Med
31. Kaufmann GR, Cunningham P, Zaunders J, et al. Impact of early HIV-1 RNA and T-lymphocyte dynamics during primary HIV-1 infection on the subsequent course of HIV-1 RNA levels and CD4+
T-lymphocyte counts in the first year of HIV-1 infection. J Acquir Immune Defic Syndr
32. Lyles RH, Munoz A, Yamashita TE, et al. Natural history of human immunodeficiency virus type 1 viremia after seroconversion and proximal to AIDS in a large cohort of homosexual men. J Infect Dis
33. Hubert JB, Burgard M, Dussaix E, et al. Natural history of serum HIV-1 RNA levels in 330 patients with a known date of infection. AIDS
34. Celum CL, Buchbinder SP, Donnell D, et al. Early human immunodeficiency virus (HIV) infection in the HIV Network for Prevention Trials Vaccine Preparedness Cohort: risk behaviors, symptoms, and early plasma and genital tract virus load. J Infect Dis
35. Graziosi C, Soudeyns H, Rizzardini GP, et al. Immunopathogenesis of HIV infection. AIDS Res Hum Retroviruses
. 1998;14(Suppl 2):S135–S142.
36. Fauci A. Host factors and the pathogenesis of HIV-induced disease. Nature
37. Haynes BF, Pantaleo G, Fauci AS. Toward an understanding of the correlates of protective immunity to HIV infection. Science
38. Hu DJ, Byers RJ, Fleming PL, et al. Characteristics of persons with late AIDS diagnosis in the United States. Am J Prev Med
39. Howard RR, Fasano CS, Frey L, et al. Reference intervals of CD3, CD4, CD8, CD4/CD8, and absolute CD4 values in Asian and non-Asian populations. Cytometry
40. Anastos K, Gange SJ, Lau B, et al. Association of race and gender with HIV-1 RNA levels and immunologic progression. J Acquir Immune Defic Syndr
41. Farzadegan H, Hoover DR, Astemborski J, et al. Sex differences in HIV-1 viral load and progression to AIDS. Lancet
42. Veugelers PJ, Page KA, Tindall B, et al. Determinants of HIV disease progression among homosexual men registered in the Tricontinental Seroconverter Study. Am J Epidemiol
43. Mulherin SA, O'Brien TR, Ioannidis JP, et al. Effects of CCR5-Delta32 and CCR2-64I alleles on HIV-1 disease progression: the protection varies with duration of infection. AIDS
44. Mellors JW, Kingsley LA, Rinaldo CR, et al. Quantitation of HIV-1 RNA in plasma predicts outcome after seroconversion. Ann Intern Med
45. De Gruttola VG, Clax P, DeMets DL, et al. Considerations in the evaluation of surrogate endpoints in clinical trials: summary of a National Institutes of Health workshop. Control Clin Trials
46. Delta Coordinating Committee and Virology Group. An evaluation of HIV RNA and CD4 cell count as surrogates for clinical outcome. AIDS
47. HIV Surrogate Marker Collaborative Group. Human immunodeficiency virus type 1 RNA level and CD4 count as prognostic markers and surrogate endpoints: a meta-analysis. AIDS Res Hum Retroviruses
48. Gilbert PB, Self SG, Ashby MA. Statistical methods for assessing differential vaccine protection against human immunodeficiency virus types. Biometrics
49. Bogard E, Kuntz KM. The impact of a partially effective HIV vaccine on a population of intravenous drug users in Bangkok, Thailand: a dynamic model. J Acquir Immune Defic Syndr
50. Timaeus IM. Impact of the HIV epidemic on mortality in sub-Saharan Africa: evidence from national surveys and censuses. AIDS
. 1998;12(Suppl 1):S15–S27.
51. Anderson RM, Swinton J, Garnett GP. Potential impact of low efficacy HIV-1 vaccines in populations with high rates of infection. Proc R Soc Lond B Biol Sci
52. Quinn TC, Wawer MJ, Sewankambo N, et al. Viral load and heterosexual transmission of human immunodeficiency virus type 1. N Engl J Med
53. Sperling RS, Shapiro DE, Coombs RW, et al. Maternal viral load, zidovudine treatment, and the risk of transmission of human immunodeficiency virus type 1 from mother to infant. N Engl J Med
54. Weidle PJ, Mastro TD, Grant AD, et al. HIV/AIDS treatment and HIV vaccines for Africa. Lancet
This article has been cited
Keywords:© 2004 Lippincott Williams & Wilkins, Inc.
HIV-1 vaccine endpoints, vaccine efficacy; early HIV disease progression