Anne Meibohm

Treatment with Indinavir, Zidovudine, and Lamivudine in Adults with Human Immunodeficiency Virus Infection and Prior Antiretroviral Therapy

BACKGROUND The new protease inhibitors are potent inhibitors of the human immunodeficiency virus (HIV), and in combination with other antiretroviral drugs they may be able to cause profound and sustained suppression of HIV replication. METHODS In this double-blind study, 97 HIV-infected patients who had received zidovudine treatment for at least 6 months and had 50 to 400 CD4 cells per cubic millimeter and at least 20,000 copies of HIV RNA per milliliter were randomly assigned to one of three treatments for up to 52 weeks: 800 mg of indinavir every eight hours; 200 mg of zidovudine every eight hours combined with 150 mg of lamivudine twice daily; or all three drugs. The patients were followed to monitor the occurrence of adverse events and changes in viral load and CD4 cell counts. RESULTS The decrease in HIV RNA over the first 24 weeks was greater in the three-drug group than in the other groups (P<0.001 for each comparison). RNA levels decreased to less than 500 copies per milliliter at week 24 in 28 of 31 patients in the three-drug group (90 percent), 12 of 28 patients in the indinavir group (43 percent), and none of 30 patients in the zidovudine-lamivudine group. The increase in CD4 cell counts over the first 24 weeks was greater in the two groups receiving indinavir than in the zidovudine-lamivudine group (P< or =0.01 for each comparison). The changes in the viral load and the CD4 cell count persisted for up to 52 weeks. All the regimens were generally well tolerated. CONCLUSIONS In most HIV-infected patients with prior antiretroviral therapy, the combination of indinavir, zidovudine, and lamivudine reduces levels of HIV RNA to less than 500 copies per milliliter for as long as one year.

Raltegravir with optimized background therapy for resistant HIV-1 infection.

BACKGROUND Raltegravir (MK-0518) is an inhibitor of human immunodeficiency virus type 1 (HIV-1) integrase active against HIV-1 susceptible or resistant to older antiretroviral drugs. METHODS We conducted two identical trials in different geographic regions to evaluate the safety and efficacy of raltegravir, as compared with placebo, in combination with optimized background therapy, in patients infected with HIV-1 that has triple-class drug resistance in whom antiretroviral therapy had failed. Patients were randomly assigned to raltegravir or placebo in a 2:1 ratio. RESULTS In the combined studies, 699 of 703 randomized patients (462 and 237 in the raltegravir and placebo groups, respectively) received the study drug. Seventeen of the 699 patients (2.4%) discontinued the study before week 16. Discontinuation was related to the study treatment in 13 of these 17 patients: 7 of the 462 raltegravir recipients (1.5%) and 6 of the 237 placebo recipients (2.5%). The results of the two studies were consistent. At week 16, counting noncompletion as treatment failure, 355 of 458 raltegravir recipients (77.5%) had HIV-1 RNA levels below 400 copies per milliliter, as compared with 99 of 236 placebo recipients (41.9%, P<0.001). Suppression of HIV-1 RNA to a level below 50 copies per milliliter was achieved at week 16 in 61.8% of the raltegravir recipients, as compared with 34.7% of placebo recipients, and at week 48 in 62.1% as compared with 32.9% (P<0.001 for both comparisons). Without adjustment for the length of follow-up, cancers were detected in 3.5% of raltegravir recipients and in 1.7% of placebo recipients. The overall frequencies of drug-related adverse events were similar in the raltegravir and placebo groups. CONCLUSIONS In HIV-infected patients with limited treatment options, raltegravir plus optimized background therapy provided better viral suppression than optimized background therapy alone for at least 48 weeks. (ClinicalTrials.gov numbers, NCT00293267 and NCT00293254.)

Subgroup and Resistance Analyses of Raltegravir for Resistant HIV-1 Infection

BACKGROUND We evaluated the efficacy of raltegravir and the development of viral resistance in two identical trials involving patients who were infected with human immunodeficiency virus type 1 (HIV-1) with triple-class drug resistance and in whom antiretroviral therapy had failed. METHODS We conducted subgroup analyses of the data from week 48 in both studies according to baseline prognostic factors. Genotyping of the integrase gene was performed in raltegravir recipients who had virologic failure. RESULTS Virologic responses to raltegravir were consistently superior to responses to placebo, regardless of the baseline values of HIV-1 RNA level; CD4 cell count; genotypic or phenotypic sensitivity score; use or nonuse of darunavir, enfuvirtide, or both in optimized background therapy; or demographic characteristics. Among patients in the two studies combined who were using both enfuvirtide and darunavir for the first time, HIV-1 RNA levels of less than 50 copies per milliliter were achieved in 89% of raltegravir recipients and 68% of placebo recipients. HIV-1 RNA levels of less than 50 copies per milliliter were achieved in 69% and 80% of the raltegravir recipients and in 47% and 57% of the placebo recipients using either darunavir or enfuvirtide for the first time, respectively. At 48 weeks, 105 of the 462 raltegravir recipients (23%) had virologic failure. Genotyping was performed in 94 raltegravir recipients with virologic failure. Integrase mutations known to be associated with phenotypic resistance to raltegravir arose during treatment in 64 patients (68%). Forty-eight of these 64 patients (75%) had two or more resistance-associated mutations. CONCLUSIONS When combined with an optimized background regimen in both studies, a consistently favorable treatment effect of raltegravir over placebo was shown in clinically relevant subgroups of patients, including those with baseline characteristics that typically predict a poor response to antiretroviral therapy: a high HIV-1 RNA level, low CD4 cell count, and low genotypic or phenotypic sensitivity score. (ClinicalTrials.gov numbers, NCT00293267 and NCT00293254.)

3-Year suppression of HIV viremia with indinavir, zidovudine, and lamivudine

The use of antiretroviral therapy that includes an HIV protease inhibitor has markedly decreased morbidity and mortality in HIV-infected persons (1-3). In addition, antiretroviral combination therapy that includes a protease inhibitor can suppress viral load levels for up to 2 years (4-8). However, the long-term durability and toxicity of these regimens are unknown. We present results after 3 years of follow-up in patients who received three-drug therapy with indinavir, zidovudine, and lamivudine in a previously reported study (4, 5). Methods Study Design The study was originally designed as a randomized, double-blind comparison of three antiretroviral regimens: indinavir (Crixivan, Merck & Co., Inc., West Point, Pennsylvania), 800 mg every 8 hours; zidovudine (Retrovir, Glaxo Wellcome, Research Triangle Park, North Carolina), 200 mg every 8 hours, with lamivudine (Epivir, Glaxo Wellcome), 150 mg every 12 hours; and all three drugs together at the same specified doses (4, 5). Patients were encouraged to drink at least 1.5 L of fluid per day. We report on the patients who were originally assigned to receive three-drug therapy. Institutional review boards at each site approved the study and amendments, and all patients gave informed consent. Study Participants Eligible patients were HIV-infected adults who had received at least 6 months of zidovudine therapy but had never taken lamivudine or an HIV protease inhibitor. Patients had serum viral load levels of at least 20 000 copies/mL (Amplicor HIV Monitor Test, Roche Diagnostic Systems, Branchburg, New Jersey) and CD4 counts between 50 to 400 cells/mm3 at screening. Assessments Patients had study visits at least every 4 weeks through week 52 and every 8 weeks through week 156. At baseline and at each visit, a history was taken, a physical examination was performed, and standardized laboratory tests were conducted without regard to food intake. Serum was processed, stored at 70 C, and subsequently assayed for HIV RNA by using the Amplicor and ultradirect assays (4, 5). T-lymphocyte subgroups were quantified by using flow cytometry. Genotypic analysis of serum HIV RNA was performed as described elsewhere (5). Individual investigators graded adverse events according to standardized guidelines. A drug-related adverse event was one that the investigator assessed as possibly, probably, or definitely related to the study therapy. Nephrolithiasis was defined as the passing of macroscopic stones or gravel or flank pain with or without associated hematuria. During follow-up, investigators assessed lipodystrophy at one time point from October to December 1998 (after approximately 2.5 to 3.5 years of treatment). Patients were considered to have lipodystrophy if they had one or more of the following features without evidence of hypercortisolemia: truncal or central obesity with or without thinning of the extremities; accumulation of body fat in the abdomen, the neck (buffalo hump), the retroperitoneum, the face, or the breasts; and accumulation or redistribution of body fat in some areas that was out of proportion to other body areas. Statistical Analysis Antiretroviral activity was assessed by calculating 1) the proportions (with 95% CIs) of patients whose HIV RNA levels were less than 500 copies/mL (Amplicor assay) and those whose HIV RNA levels were less than 50 copies/mL [ultradirect assay] and 2) the median changes (plus interquartile ranges) from baseline in log10HIV RNA levels (Amplicor assay) and CD4 cell counts over time. Analyses were performed on an intention-to-treat basis. Patients who withdrew early from the study were considered to have had virologic failure at subsequent time points, except for two patients who withdrew for reasons that were not related to therapy and had HIV RNA levels less than 500 copies/mL at the time of withdrawal, as described elsewhere (5). Because the analyses included patients with observed values and those with imputed values, the term contributing patients is used. Among patients with at least two measurements, those who never achieved an HIV RNA level less than 500 copies/mL were considered to have experienced virologic failure. Those who achieved an HIV RNA level less than 500 copies/mL were considered to have experienced virologic failure if they had two consecutive measurements of HIV RNA levels that were at least 500 copies/mL but did not have subsequent re-suppression while receiving the same three-drug regimen. Role of the Study Sponsor Employees of the industry sponsor participated in the study as co-investigators. After designing the study with the input of the other study investigators, these employees implemented the protocol and coordinated data collection and statistical analyses. All investigators interpreted the data, determined the content of the paper, and decided whether to submit the paper for publication. Results Study Participants Originally, 33 patients were randomly assigned to receive three-drug therapy with zidovudine, lamivudine, and indinavir. Median age was 40 years (range, 30 to 62 years). Thirty-one patients (94%) were men, and 2 (6%) were women; 26 (79%) were white, 2 (6%) were African American, 3 (9%) were Latin American, and 2 (6%) were members of other racial or ethnic groups. At study entry, patients had taken zidovudine for a median of 28 months (range, 6 to 92 months) and had a median baseline serum HIV RNA level of 41 900 copies/mL (range, 7550 to 219 040 copies/mL) and a median baseline CD4 count of 133 cells/mm3 (range, 35 to 433 cells/mm3). Of the 33 patients, 12 (36%) discontinued therapy within 3 years: 7 because of increased viral load levels; 2 because of need for contraindicated medications (rifampin and cytotoxic chemotherapy); and 1 each because of nausea, patient request, and investigator recommendation after resolution of urinary tract obstruction. Nine patients experienced virologic failure (6 in the first year, 0 in the second year, and 3 in the third year). Antiretroviral Activity The percentages of contributing patients whose HIV RNA level decreased from baseline to less than 500 copies/mL and less than 50 copies/mL, respectively, were 78% (95% CI, 60% to 90%) and 75% (CI, 56% to 88%) at 1 year, 78% (CI, 60% to 90%) and 66% (CI, 47% to 81%) at 2 years, and 68% (CI, 49% to 83%) (21 of 31 patients) and 65% (CI, 45% to 80%) (20 of 31 patients) at 3 years (Figure 1). Patients experienced a median change in HIV RNA level from baseline of 2.07 log10 copies/mL (interquartile range, 2.39 to 1.61 log10 copies/mL) at 1 year, 2.07 log10 copies/mL (interquartile range, 2.40 to 1.61 log10 copies/mL) at 2 years, and 1.99 log10 copies/mL (interquartile range, 2.32 to 1.31 log10 copies/mL) at 3 years (Figure 2). The median increase in CD4 counts from baseline was 155 cells/mm3 (interquartile range, 95 to 230 cells/mm3) at 1 year, 209 cells/mm3 (interquartile range, 117 to 339 cells/mm3) at 2 years, and 230 cells/mm3 (interquartile range, 150 to 316 cells/mm3) at 3 years (Figure 2). Figure 1. Proportion of patients with serum HIV RNA levels less than 500 copies/mL and less than 50 copies/mL during 3 years of treatment with indinavir, zidovudine, and lamivudine. Figure 2. Median changes in serum HIV RNA level and CD4 cell count from baseline during 3 years of treatment with indinavir, zidovudine, and lamivudine. Genotypic Analysis of Viral Resistance For the nine patients who experienced virologic failure by year 3, results of genotypic analyses performed at baseline and at the time of virologic failure were similar to the results of the 2-year analysis (5). Briefly, six patients had preexisting zidovudine resistance, as evidenced by the presence of the reverse transcriptase T215Y substitution combined with M41L (four patients), K70R (one patient), or D67N/K70R/K219Q (one patient). One patient developed zidovudine resistance (M41L), and all nine developed lamivudine resistance (M184V). Five patients acquired protease substitutions that were previously associated with indinavir resistance (9): M46L/V82A (four patients) and L90M (one patient). In two additional patients, evidence of new protease substitutions (L10V or L63P/S/A) appeared during treatment; however, the significance of substitutions at these two naturally occurring polymorphic sites is unclear. Adverse Events Four patients experienced a serious drug-related adverse event related to nephrolithiasis. Two of these patients experienced urinary tract obstruction, and one withdrew from the study 2 months after the adverse event resolved. Two of these patients also had other serious drug-related adverse events (pain and abdominal pain). In total, 12 of 33 patients (36%) had at least one episode of clinical nephrolithiasis during 3 years of treatment and 7 of 33 patients (21%) had more than one episode. Initial episodes of nephrolithiasis occurred from 24 weeks to 3 years of treatment. A total of 64.6 person-years of follow-up occurred before the first episode of nephrolithiasis or before censoring at 3 years. Therefore, the incidence of nephrolithiasis was 1.86 per 10 person-years of follow-up. Of the 21 patients in active follow-up, 4 (19%) fulfilled the clinical definition of lipodystrophy. When random, nonfasting specimens obtained throughout the study were used, serum triglyceride levels greater than 8.47 mmol/L (750 mg/dL) were documented at least once in 8 of 33 patients (24%) and levels greater than 13.55 mmol/L (1200 mg/dL) were documented in 2 of 33 patients (6%). Serum glucose levels greater than 13.88 mmol/L (250 mg/dL) occurred at least once in 1 of 33 patients (3%). Total serum cholesterol level was measured retrospectively in frozen samples obtained after 0.5, 1, 2, and 3 years of follow-up. Seven of 30 patients (23%) had total serum cholesterol levels of at least 6.21 mmol/L (240 mg/dL), and 1 of 30 patients (3%) had a level of at least 7.76 mmol/L (300 mg/dL) at least once. Discussion Evidence shows that it will be difficult

Random-Effects Meta-analysis of Inconsistent Effects: A Time for Change

Key Summary Points The decision to calculate a summary estimate in a meta-analysis should be based on clinical judgment, the number of studies, and the degree of variation among studies. A random-effects model is a meta-analytic approach that incorporates study-to-study variability beyond what would be expected by chance. The DerSimonianLaird (DL) method, the earliest and most commonly used random-effects model, is the default method in many software packages. The DL method produces confidence bounds that are too narrow (and P values that are typically too small) when the number of studies is small or when there are substantive differences among study estimates. Alternative random-effects estimates based on small-sample adjustments, the profile likelihood, or hierarchical Bayesian models that perform better than the DL method are readily available in software packages. When it is appropriate to pool studies whose estimates vary widely, meta-analytic methods that provide a better accounting of uncertainty than the DL estimator should be used. The basic premise of meta-analysis is that the average of estimates provided by a group of studies is closer to the truth than the estimate provided by an individual study. This premise rests on the assumption that each study is a near-replication of a single experiment and that differences among study results are due only to chance. The technical jargon for this fundamental assumption is that each of the studies is estimating the same fixed effect, and the corresponding meta-analytic approach is dubbed the fixed-effects model. When studies are statistically heterogeneous and differences among their results cannot be explained by chance alone, the meta-analyst faces a conundrum. Qualitative heterogeneity among study designs, patient characteristics, and treatment and comparator regimens may be so great that it does not make sense to combine studies to derive a single summary estimate. However, when the qualitative and quantitative heterogeneity is not so great that a single number summarizing the evidence would be misleading, statistical models that incorporate the extra variability across studies not believed to be due to chance may be used to summarize the data. These models assume that the observed treatment effect for a study is a combination of a treatment effect common to all studies plus a component specific to that study alone. This extra, study-specific component is assumed to be random, hence the jargon that it is a random effect, with accompanying mathematical models dubbed random-effects models. The most widely used random-effects model is based on an estimator developed by DerSimonian and Laird in the mid-1980s and is known as the DerSimonianLaird (DL) estimator (1). Supplement. Alternative Random Effects Estimators An Example The Figure depicts a statistically heterogeneous set of studies followed by several methods of estimating their average effect. The example is from a 1985 meta-analysis by Collins and colleagues on the effect of administering a diuretic to women at risk for preeclampsia (11), and it is frequently used to illustrate different methods for estimating a common treatment effect when the body of evidence is heterogeneous (12, 13). The effect estimates from the individual studies range from a more than 4-fold statistically significant decrease in the odds of eclampsia with diuretics observed in the study by Fallis and colleagues (5) to an almost 3-fold nonsignificant increase in the study by Tervil and Vartiainen (9). A visual clue that these studies are statistically heterogeneous is that the confidence limits of several pairs of studies do not overlap. Figure. Heterogeneous evidence from Collins and colleagues meta-analysis of the effects of diuretics on preeclampsia (11). * The metafor package in R was used to compute the fixed-effects estimate and the DerSimonianLaird random-effects estimate. The metafor package in R was used to compute the KnappHartung small-sample adjustments, based on the DerSimonianLaird estimate. The small-sample (Skovgaard) estimate from the metaLik package in R was used to compute the profile likelihood estimate. The large-sample profile likelihood estimate produced a narrower CI that indicates a statistically significant effect (95% CI, 0.37 to 0.95). The hierarchical Bayesian estimate was computed using WinBugs and assumed a vague uniform (10, 10) prior distribution for . A sensitivity analysis assuming a vague (0.001, 0.001) on precision (1/2) produced a slightly smaller but statistically significant 95% CI (0.36 to 0.98). The Figure shows that different statistical approaches to combining data can produce results leading to different conclusions. The fixed-effects model, which is not appropriate for these data, shows a summary effect of 0.67, with 95% confidence limits (0.56 and 0.80) that are 19% less than and greater than that value. The DL random-effects estimate shows a slightly larger effect (odds ratio, 0.60), but the confidence limits are substantially wider33% less than (0.40) and greater than (0.89) the summary effect, albeit still highly statistically significant. Use of any of the other 3 random-effects estimators depicted in the Figure shows identical point estimates for the odds ratio of 0.60 but dramatically wider confidence limits that are 73% less than and greater than 0.60, with the upper limits all exceeding 1.00. The corresponding P values range from less than 0.001 for the fixed-effects model to 0.011 for the DL estimator and 0.070 or greater for the other random-effects models. Statistical Heterogeneity and Uncertainty The differences noted in the example are due to the ways that the models handle statistical heterogeneity. Statistical heterogeneity refers to variation in the true effects being estimated by each study. We characterize this variation by its SD, a statistic called . Assuming normality, we expect 95% of true effects to fall within2 of the central estimate. When odds ratios or relative risks are used, the normality is on a log scale, so that true study odds ratios or relative risks fall within a range of the estimate multiplied by e2. In the example, equals 0.48, so the true study effects are estimated to fall within 0.60e0.96, or 2.6 times greater than or less than 0.60 (0.23 to 1.56). This range should be smaller than the actual smallest and largest study estimates, as is the case in this example, with the remainder of the variation assumed to be due to chance. The models vary in their assumption of how certain we are about ; this uncertainty is included in the meta-analytic CIs. The DL method assumes that our guess about is exactly correct, with no uncertainty; thus, confidence limits are too narrow and the P values are too small. In Collins and colleagues meta-analysis, which pooled a modest number of studies (n= 9) with statistically heterogeneous effects, the DL estimator provided the narrowest confidence limits among the random-effects options. In addition to , meta-analysts commonly use statistical tests, such as the Cochran Q test, or indices, such as the I 2 index, to help gauge heterogeneity of effects. Both the Cochran Q test and the I 2 index are dimensionless measures of statistical heterogeneity. Neither conveys information about actual variation in effect size, and both have low power to detect heterogeneity in situations involving 10 or fewer studies (14). The DL Estimator and Alternative Approaches The DL method appeared in the literature just as meta-analytic methods were being adopted to help reviewers quantitatively summarize evidence about medical interventions. It was relatively simple to compute and is still the standard estimator programmed into many meta-analysis software packages, including the RevMan software developed by the Cochrane Collaboration (15). As statisticians began in the 1990s to recognize the problems with the DL approach, theyincluding DerSimonian and Kacker (16)proposed a wide range of alternatives that better capture the uncertainty associated with statistical heterogeneity. These included random-effects estimators based on small-sample adjustments, such as the KnappHartung approach (17), likelihood-based methods (13, 18, 19), and hierarchical Bayesian models (20). The KnappHartung approach, one of the more recent methods, assumes that variances are estimated from small samples, makes small-sample adjustments to the variance estimates, and constructs confidence limits based on the t distribution with k 1 degrees of freedom. This estimator produces a wider confidence limit than the DL estimate. It may slightly overestimate the amount of uncertainty in some cases, particularly when dealing with 5 or fewer studies. It is available in some specialized meta-analysis programs and packages, such as the metareg program (21) in Stata (StataCorp, College Station, Texas) and the metafor package (22) in R (R Foundation for Statistical Computing, Vienna, Austria). Likelihood estimates, which are readily available in such commonly used statistical packages as SAS (SAS Institute, Cary, North Carolina), are computed using standard mixed-effects linear models (18, 19). The profile likelihood is a good method for computing confidence bounds. Unlike estimators based on maximum likelihood or restricted maximum likelihood methods, the profile likelihood allows for asymmetrical intervals and uncertainty in estimation of the between-study variance (2). Simulation studies show that it provides a substantially better accounting of uncertainty than the DL estimator (13, 23). The profile likelihood estimates are available in the metaan package (24) in Stata and the metaLik package (25) in R. The latter provides a more accurate but possibly conservative small-sample profile likelihood estimate of uncertainty (26). Bayesian random-effects models, which are based on an exact binomial distribution, perform well in many situations where others do poorly, particularl

Residual human immunodeficiency virus (HIV) Type 1 RNA and DNA in lymph nodes and HIV RNA in genital secretions and in cerebrospinal fluid after suppression of viremia for 2 years.

Residual viral replication persists in a significant proportion of human immunodeficiency virus (HIV)-infected patients receiving potent antiretroviral therapy. To determine the source of this virus, levels of HIV RNA and DNA from lymphoid tissues and levels of viral RNA in serum, cerebrospinal fluid (CSF), and genital secretions in 28 patients treated for < or =2.5 years with indinavir, zidovudine, and lamivudine were examined. Both HIV RNA and DNA remained detectable in all lymph nodes. In contrast, HIV RNA was not detected in 20 of 23 genital secretions or in any of 13 CSF samples after 2 years of treatment. HIV envelope sequence data from plasma and lymph nodes from 4 patients demonstrated sequence divergence, which suggests varying degrees of residual viral replication in 3 and absence in 1 patient. In patients receiving potent antiretroviral therapy, the greatest virus burden may continue to be in lymphoid tissues rather than in central nervous system or genitourinary compartments.

A Randomized, Controlled Trial of Indinavir, Zidovudine, and Lamivudine in Adults with Advanced Human Immunodeficiency Virus Type 1 Infection and Prior Antiretroviral Therapy

A randomized, double-blind, multicenter study of indinavir, zidovudine, and lamivudine was conducted in 320 adults with human immunodeficiency virus type 1 (HIV-1) infection, </=50 CD4 cells/mm3, and extensive prior zidovudine therapy. Patients received indinavir, 800 mg every 8 h; zidovudine, 200 mg every 8 h, and lamivudine, 150 mg twice daily; or all 3 drugs for 24 weeks. In an intention-to-treat analysis, proportions of patients with HIV-1 RNA <500 and <50 copies/mL, respectively, at week 24 were 56% and 45% in the indinavir-zidovudine-lamivudine group, 3% and 2% in the indinavir group, and 0% in the zidovudine-lamivudine group. Observed mean CD4 cell increases were 95, 78, and 6 cells/mm3 in the three-, one-, and two-drug arms, respectively. Regimens were generally well tolerated. Patients with advanced HIV-1 infection benefit from triple therapy with indinavir, zidovudine, and lamivudine, although the proportion with optimal response appeared to be lower in patients with low CD4 cell counts.

Clinical trial and post-licensure safety profile of a prophylactic human papillomavirus (types 6, 11, 16, and 18) l1 virus-like particle vaccine.

Background: We describe the safety of the human papillomavirus (HPV)-6/11/16/18 vaccine using updated clinical trial data (median follow-up time of 3.6 years) and summarize up to 3 years of post-licensure surveillance. Methods: In 5 clinical trials, 21,480 girls/women aged 9 to 26 years and boys aged 9 to 16 years received ≥1 dose of HPV-6/11/16/18 vaccine or placebo. All serious and nonserious adverse experiences (AEs) and new medical conditions were recorded for the entire study period(s). As of June 2009, >25 million doses of HPV-6/11/16/18 vaccine had been distributed in the United States with >50 million doses globally. Post-licensure safety as summarized by the Centers for Disease Control and Prevention using the United States Vaccine Adverse Event Reporting System database is also reported. Results: Eight subjects experienced a treatment-related serious AE (0.05% vaccine; 0.02% placebo). Of 18 deaths (0.1% vaccine; 0.1% placebo), all were considered unrelated to study treatment. New medical conditions which were potentially consistent with autoimmune phenomena were reported in 2.4% of both vaccine and placebo recipients. Pain, the most common injection-site AE, occurred more frequently with vaccine (81% vaccine; 75% placeboaluminum; 45% placebo-saline). No differences were seen in the incidence of the most common nonserious AEs–headache and pyrexia. The Vaccine Adverse Event Reporting System has received 14,072 reports for the HPV-6/11/16/18 vaccine since licensure, with only 7% being serious AEs, about half the average reported for licensed vaccines in general. Conclusions: HPV-6/11/16/18 vaccination was associated with more injection-site pain than placebo but similar incidences of systemic and serious AEs and new medical conditions potentially consistent with autoimmune phenomena. Based on review of post-licensure safety information, the benefits of vaccination to prevent the majority of genital tract precancers and cancers continue to far outweigh its risks.

Modeling the Long-Term Outcomes and Costs of HIV Antiretroviral Therapy Using HIV RNA Levels: Application to a Clinical Trial

A model was developed to gain insight into the potential clinical and economic impact of antiretroviral therapy for HIV-infected patients. Observed HIV RNA levels and CD4 cell counts are used in the model to estimate the probability that an individual progresses from asymptomatic infection to the first AIDS-defining illness and death and to estimate the total net cost of care and long-term cost-effectiveness of antiretroviral therapy. The model was applied to patients in a clinical trial (Merck protocol 035) that compared the surrogate marker response to triple therapy with indinavir (IDV; 800 mg every 8 hr) plus zidovudine (ZDV; 200 mg every 8 hr) plus lamivudine (3TC; 150 mg twice a day) to double therapy with ZDV+3TC. The model projected that for an individual without AIDS who received triple therapy the progression to AIDS and death would be delayed more than for a patient who received double therapy with ZDV+3TC if no other treatment options were offered. Because of this delay in disease progression, the total discounted cost over the initial 5-year period was projected to be

Factors Influencing the Emergence of Resistance to Indinavir: Role of Virologic, Immunologic, and Pharmacologic Variables

5100 lower for patients who received triple therapy compared with double therapy if suppression with triple therapy lasts up to 3 years. If suppression with triple therapy lasts up to 5 years, costs were projected to be higher with the triple combination, but 81% of the cost is offset by lower disease costs as a result of fewer patients progressing to AIDS. Over 20 years, total discounted cost was projected to be higher for the triple-therapy regimen primarily because of a longer estimated survival time. At 20 years, the incremental cost per life-year gained by adding IDV to a ZDV+3TC regimen was estimated at