The nonnucleoside reverse transcriptase inhibitors (NNRTIs) have a well established role in HIV therapy. Efavirenz and nevirapine, first-generation NNRTIs, are commonly prescribed as first-line or second-line therapy . However, a major disadvantage of the first-generation NNRTIs is that only a single mutation is required to confer resistance to all of them . Etravirine is a second-generation NNRTI that is effective when used in adults who are treatment resistant to the first-generation NNRTIs [3,4] because it has a unique resistance profile and typically requires multiple resistance-associated mutations to lose efficacy .
Two randomized, controlled, double-blinded phase III studies (DUET 1 and DUET 2) were completed in a treatment-experienced population that had at least one documented NNRTI resistance-associated mutation and at least three primary protease inhibitor mutations at screening. These studies compared etravirine [200 mg twice daily (b.i.d.)] to placebo (b.i.d.), each with a background regimen consisting of darunavir/ritonavir (darunavir/r; 600/100 mg b.i.d.), at least two nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), and optionally the fusion inhibitor enfuvirtide. The etravirine group achieved viral loads less than 50 copies/ml at a significantly higher rate and had significantly higher mean CD4 cell count increases at weeks 48 and 96 than the placebo group [3,4]. At 48 weeks, the etravirine group also experienced better quality of life and significantly fewer hospital days and AIDS-defining events/deaths [3,6,7].
In this article, we present an economic evaluation of the use of etravirine in treatment-experienced adults with HIV-1 infection in Canada. This evaluation includes a 1-year cost-effectiveness analysis using a well accepted surrogate endpoint , adults achieving viral load less than 50 copies/ml, and a lifetime cost-utility analysis, the results of which were tested extensively in uncertainty and variability analyses.
A Markov model with a 3-month cycle time  was adapted to follow disease progression in a hypothetical cohort of treatment-experienced adults over their remaining lifetimes. The modeled cohort had demographic and disease-severity characteristics that matched those of participants in the DUET 1 and DUET 2 clinical trials. Individuals entered the model in one of six health states defined by CD4 cell count ranges (0–50, 51–100, 101–200, 201–350, 351–500, >500 cells/μl). Every 3 months, individuals could move between CD4 cell count ranges with probabilities determined by the natural history of disease modified by response to treatment. In any 3-month cycle, individuals could also die due to causes related or unrelated to HIV.
The base-case economic evaluation compared two initial treatment regimens, etravirine plus a background regimen consisting of darunavir/r, at least two NRTIs, and optionally enfuvirtide versus the same darunavir/r-based background regimen without etravirine. NRTI and enfuvirtide use in the model was based on use in the DUET 1 and DUET 2 clinical trials as determined by investigators based on resistance testing and treatment history. After failure of the initial regimen, modeled individuals switched to a subsequent regimen that included the integrase strand transfer inhibitor raltegravir and optimal protease inhibitors, NRTIs, and fusion inhibitors. The model assumed that individuals remained on the switch regimen until death, despite virologic failure (Fig. 1). Other treatment patterns were tested in variability analyses.
After beginning a new treatment regimen, the model categorized individuals into one of three virologic-response groups at 24 weeks: complete suppression (<50 copies/ml), partial suppression (50 copies/ml to ≥1-log10 drop in viral load), and no suppression (<1-log10 drop in viral load). These virologic-response categories and the timing of the assessment (i.e., 24 weeks) were chosen because of the correlation between early virologic response and both the magnitude of increase in CD4 cell count and the likely duration of response to treatment [1,9]. Modeled CD4 cell count increases varied by virologic-response group. After starting the initial regimen, individuals in the model experienced two phases of CD4 response to approximate the response curves observed in the DUET 1 and DUET 2 clinical trials, rapidly increasing CD4 cell count followed by more slowly changing CD4 cell count. Individuals then switched therapy and experienced the two phases of CD4 cell count response, followed by a third phase characterized by declining CD4 cell count.
The analysis took a Canadian Provincial Ministry of Health perspective, evaluating the impact of etravirine on direct, reimbursable healthcare costs and patient well-being and life expectancy. Modeled individuals in each CD4 cell count range incurred costs based on estimates of healthcare services used, including antiretroviral and other drugs, hospital stays, emergency room visits, and outpatient visits and tests. Each CD4 cell count range was assigned a utility weight that quantified individual well-being on a scale ranging from 0 (worst possible health) to 1 (perfect health). The utility values were used to convert the time spent in each health state over an individual's remaining lifetime into estimates of quality-adjusted life-years (QALYs).
The only significant difference in adverse events between the etravirine and the control groups in the DUET 1 and DUET 2 clinical trials at 48 weeks was the occurrence of rash, which was generally mild and resolved within 1–2 weeks . Hence, the model did not make specific adjustments to costs and quality of life to account for differences in adverse events.
Input parameter values
Characteristics of the population entering the model were based on the intention-to-treat population enrolled in the DUET 1 and DUET 2 clinical trials [3,10]. The population was 89.3% men and was distributed into three age categories: 18–39 years (18.9%), 40–64 years (79.9%), and 65+ years (1.2%). The distribution of participants in the six CD4 cell count ranges is shown in Table 1 [10–14]. Alternative population characteristics, based on treatment-experienced individuals in the British Columbia Centre for Excellence in HIV/AIDS Drug Treatment Program, were tested in variability analyses.
The percentages of individuals in each of the three virologic-response categories for the etravirine and control arms of the model were based on 24-week results observed in the intention-to-treat population of the DUET 1 and DUET 2 clinical trials, stratified by enfuvirtide use . For the raltegravir-based switch regimen, published data from the BENCHMRK 1 and BENCHMRK 2 trials were used to estimate virologic efficacy at 24 weeks (Table 2) [3,10,15–23].
Duration of efficacy
The lengths of time spent by individuals in the two phases of CD4 cell count response for the etravirine and control arms of the model were based on clinical trial data (Table 2) [3,10]. For all virologic-response groups, the period of rapid CD4 cell count increase was set to 6 months to reflect the rapid increases observed in both clinical trial arms in the first 24 weeks of treatment. After week 24, the rate of change was slower. For the less than 50 copies/ml virologic-response group, the period of slowly changing CD4 cell count was estimated by extrapolating Weibull curves fit to the observed Kaplan–Meier discontinuation plots for each trial arm up to week 96 . The resulting times were 3.25 years in the etravirine arm and 1.75 years in the control arm. The period of slowly changing CD4 cell count for the other two virologic-response groups was set to 6 months (for a total time on initial therapy of 1 year), at which time partially suppressed and unsuppressed individuals were assumed to switch to an alternate regimen.
The durations for the raltegravir-based switch regimen were assumed to be the same as those for the etravirine and control regimens in all virologic-response groups, with the exception of the duration of slowly changing CD4 cell count in the less than 50 copies/ml group. This duration was set to 2 years, on the basis of long-term studies indicating continuing increases in CD4 cell count over several years for individuals who achieved undetectable viral load, for example, Hunt et al. , but an assumed shorter duration of efficacy for the switch regimen than for the initial etravirine regimen (Table 2). After failing the switch regimen, individuals were assumed to experience declining CD4 cell count for the remainder of their lifetimes.
Transition probabilities between CD4 cell count health states
The probabilities of moving between the CD4 cell count health states in a 3-month period during the two phases of CD4 cell count response were estimated by treatment group from the DUET 1 and DUET 2 clinical trial data. Specifically, the probabilities by treatment and for each virologic-response group were estimated using the means and SDs of the CD4 cell count changes observed at 24 and 48 weeks from the intention-to-treat population (Table 2) .
The CD4 cell count responses by virologic-response category were not published for the raltegravir-based switch regimen; thus, these values were imputed from the published data [15–17], assuming similar patterns among the virologic-response categories to those observed with etravirine (Table 2). After treatment failure on the switch regimen, the rate of declining CD4 cell count (27.81 cells/μl per year) was estimated from results of observational studies (see Section 1, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting the calculation of the rate of CD4 decline).
Utility weights and AIDS-defining illnesses
Utility weights by CD4 cell count range were taken from a study that estimated values using responses to the EuroQol questionnaire from 21 000 participants in HIV clinical trials, including participants in Canada (Table 1) . Mean annual AIDS-defining illnesses by CD4 cell count range were obtained from a study of the EuroSIDA observational cohort (Table 1) .
Individuals in the model were at risk for death in each 3-month cycle from causes related or unrelated to HIV. In the first year of the model, mortality was based on deaths observed in the DUET 1 and DUET 2 clinical trials (Table 2) (see Section 2, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting the calculation of first-year mortality transition probabilities). For subsequent model years, the probabilities of HIV-related death varied by CD4 cell count range and were converted from rates observed in the EuroSIDA observational cohort (Table 1) . Annual probabilities of non-HIV-related death were calculated for each of three modeled age groups (18–39 years: 0.0010; 40–64 years: 0.0033; and 65+ years: 0.0188) by applying a relative risk value of 2.5  to age-specific and sex-specific Canadian national statistics on general population mortality . The relative risk value accounted for higher non-HIV-related death rates among individuals with HIV infection than among the general population, partly due to higher rates of smoking, higher rates of accidental death due to drug overdose, and higher rates of death from hepatitis . To allow the cohort to age, an adjustment factor estimated from the Canadian general population mortality statistics (1.0194) was applied in each 3-month cycle.
Resource use and costs
Antiretroviral therapy costs were based on the proportions of individuals using each antiretroviral drug in the DUET 1 and DUET 2 clinical trials for the etravirine and control initial regimens  and in the BENCHMRK 1 and BENCHMRK 2 clinical trials for the raltegravir-based switch regimen [17,20]. Unit costs in 2009 Canadian dollars were obtained from standard Canadian sources, including the Ontario Ministry of Health and Association Québécoise des Pharmaciens Propriétaires drug benefit lists [21,22] (see Section 3, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting details about antiretroviral drug use and costs). The daily cost for etravirine was $21.80. Average daily and 3-month costs for each regimen are shown in Table 2.
First-year inpatient costs were based on hospital days observed in the first 48 weeks of the DUET 1 and DUET 2 clinical trials (Table 2) , and non-antiretroviral therapy drug costs were obtained from a Canadian cost study (Table 1) . Other medical costs were calculated by applying Canada-specific unit costs [27,28] to the results of an unpublished resource use study from the British Columbia Centre for Excellence in HIV/AIDS (similar to Yip et al. ) that estimated hospital days, physician visits, emergency department visits, and laboratory tests by CD4 cell count range (Table 1) (see Section 4, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting details about the resource use study). All costs were inflated to 2009 Canadian dollars, using the health and personal care component of the Canadian consumer price index.
The model was used to conduct both short-term and long-term analyses. For the short-term cost-effectiveness analysis, the primary outcome was the 1-year incremental cost per additional person with a viral load less than 50 copies/ml at 48 weeks. For the long-term cost-utility analysis, all costs and health outcomes were discounted at 5%, and the primary outcome was the incremental lifetime cost per QALY gained.
Uncertainty and variability analyses and model validation
Extensive analyses were performed to assess the impact of parameter uncertainty on the results of the lifetime cost-utility analysis. Input parameter values were varied across realistic ranges in one-way sensitivity analyses. In addition, input parameter values were varied simultaneously in a probabilistic sensitivity analysis that sampled input values from appropriate probability distributions via Monte Carlo simulation. The ranges and distributions used in the uncertainty analyses were derived from published sources whenever possible (see Section 5, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting a table of ranges and distributions). Variability analyses, focusing on model assumptions about patient characteristics, treatment patterns, time horizon, and discount rates, were also conducted. Variability in these model assumptions does not reflect uncertainty about the parameter values but, rather, provides the decision-maker with estimates of the value of etravirine for alternative scenarios. Finally, selected model outcomes were validated using observed and modeled data (see Section 6, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, describing the model validation).
Base-case results: short-term cost-effectiveness analysis
Pooled results of the DUET 1 and DUET 2 clinical trials showed that 60.6% of individuals who received etravirine had viral loads less than 50 copies/ml at 48 weeks, compared with 39.7% in the control arm . The 1-year cost-effectiveness analysis showed that the total additional cost associated with the etravirine regimen was $4979, resulting in a 1-year incremental cost per additional person with a viral load less than 50 copies/ml at 48 weeks of $23 862 (Table 3).
Base-case results: lifetime cost-utility analysis
The lifetime cost-utility analysis showed that individuals who received the etravirine regimen accrued more life-years and QALYs, experienced fewer AIDS-defining illnesses, and spent more time in the higher CD4 cell count ranges than individuals receiving the control regimen. Lifetime costs increased by $32 198, resulting in an incremental cost of $50 432 per life-year gained and $49 120 per QALY gained (Table 3).
Results of uncertainty analyses for the lifetime cost-utility analysis
The results of the one-way sensitivity analyses showed that the incremental cost per QALY gained ranged from $38 113 to 66 249. Results were most sensitive to changes in the rates and durations of the CD4 cell count increases in the etravirine and control initial regimens and to the HIV-related mortality rate for each CD4 cell count range (see Fig. 2).
The probabilistic sensitivity analysis showed that the etravirine regimen was cost-effective in 50.3% of simulation runs for a willingness-to-pay threshold of $50 000 per QALY gained and in 82.3% of runs for a threshold of $100 000 per QALY gained (see Section 7, Supplemental Digital Content 1, http://links.lww.com/QAD/A191, presenting the cost-effectiveness acceptability curve).
Results of variability analyses for the lifetime cost-utility analysis
Results of the variability analyses found that the model results changed most substantially when the model time horizon was shorter. Model results were less sensitive to changes in population characteristics, treatment patterns, and other model assumptions. However, when treatment patterns were varied so that the comparator or switch regimens were less effective or enfuvirtide use was lower, the cost-utility ratio for etravirine was more favorable than in the base case (Table 4) [10,29–33].
This analysis showed that adding etravirine to a regimen containing darunavir/r, at least two NRTIs, and optional enfuvirtide for treatment-experienced adults with HIV-1 infection in Canada resulted in an incremental 1-year cost per additional individual with a viral load less than 50 copies/ml at 48 weeks of $23 862 and an incremental lifetime cost per QALY gain of $49 120. Although standard willingness-to-pay thresholds have not been established with which to compare the short-term results, the long-term results are within commonly cited willingness-to-pay thresholds in Canada and the United States (i.e., thresholds of $50 000–100 000 per QALY gained) [34,35]. The lifetime cost-utility analysis also showed that individuals in the etravirine arm were predicted to live 0.64 years (1.51 years undiscounted) longer than individuals in the control arm, at an additional discounted lifetime cost of $32 198. The additional life expectancy resulted from the higher rate of virologic suppression and longer average time on initial therapy for etravirine. The additional cost resulted largely from the need for continued antiretroviral therapy and other healthcare services during the additional years alive. The mean annual discounted cost was similar between arms, $48 077 in the etravirine arm and $47 933 in the control arm. The analysis further showed that individuals in the etravirine arm spent more time in the upper CD4 cell count ranges, in which well-being is greater and costs are lower, and less time in the lower CD4 cell count ranges than individuals in the control arm.
Results of the one-way and probabilistic sensitivity analyses indicated that the lifetime cost-utility analysis results were robust to input parameter uncertainty. In particular, model results were robust to reductions in the efficacy of etravirine and reductions in mortality rates from HIV-related and non-HIV-related causes, with cost-utility ratios always remaining under $100 000 per QALY gained. Variability analyses found that the cost-utility ratio was higher when shorter time horizons were modeled, up to $222 085 at 5 years. The incremental investment in treatment with etravirine yields both a near-term clinical benefit, as observed in the analysis of incremental cost per additional individual with a viral load less than 50 copies/ml at 48 weeks, and a long-term survival benefit. However, as the survival benefit is not realized in the near term, variability analyses with shorter time horizons result in higher cost-utility ratios. The model results were much less sensitive to the other scenarios tested in the variability analyses, including alternative treatment patterns, population characteristics, and discount rates, indicating that etravirine is likely to be cost-effective in a variety of practice environments.
We compared our results with recent cost-utility studies in treatment-experienced populations for raltegravir, maraviroc, and enfuvirtide. The studies identified were not directly comparable with our analysis because of methodological differences between the models; most notably, these models utilized comparator and switch regimens largely or completely composed of older protease inhibitors and a background regimen, whereas our model included a darunavir/r-based comparator regimen and a raltegravir-based switch regimen. To provide more comparable cost-utility estimates, our variability analysis included scenarios with either 100% older protease inhibitors or 50% older protease inhibitors and 50% darunavir/r as the protease inhibitor component of both the comparator and switch regimens, yielding cost-utility ratios for etravirine of $29 856 or 35 458 per QALY gained, respectively (Table 4). These values were similar to or lower than reported cost-utility ratios from US studies of maraviroc ($56 443) and enfuvirtide ($69 500), and Swiss and Spanish studies of raltegravir (approximately $41 500 when converted to US or Canadian dollars) [36–39].
Our analysis had limitations that should be considered when interpreting the results. Long-term follow-up data for the regimens included in the model were not available. In our study, continuing CD4 cell count increases and duration of efficacy for individuals achieving a viral load of less than 50 copies/ml were estimated based on the trial 96-week data [4,10]. Published results from observational data and clinical trials have shown continuing increases in CD4 cell count of up to 100 cells/μl per year for up to 4 years with continuing virologic suppression, for example, Hunt et al. .
A second limitation of the analysis was the use of results from a phase III clinical trial that excluded patients with certain comorbidities, including severe liver disease and a currently active AIDS-defining illness. Trial eligibility criteria also required at least one NNRTI resistance-associated mutation and at least three primary protease inhibitor mutations .
A third limitation of the analysis is that, although the etravirine phase III clinical trials did include an active control arm, this control arm did not include raltegravir or maraviroc because they were not available at the time the trial was initiated. It was not possible to compare directly the etravirine combination regimen with raltegravir and maraviroc regimens because of differences in the drug regimens used in the control arms in the phase III trials and possible differences in the included populations, as illustrated by variations in virologic-response rates among the control groups in the trials [3,20,40].
Additional limitations of this analysis are related to the therapy switch after the initial etravirine regimen. First, the efficacy of the raltegravir-based switch regimen when used after an etravirine-based initial regimen is not known. However, the one-way sensitivity analysis showed that the model's results were not sensitive to changes in the efficacy of the switch regimen. Second, the model was not structured to test the impact of individuals discontinuing etravirine (for tolerability reasons, for example) but choosing to remain on the darunavir/r-based control regimen. However, because both costs and benefits would match the control group for the population subset discontinuing from etravirine only, the impact on the cost-effectiveness results would be small.
This study uses a Markov model populated with clinical trial and other published data and shows that etravirine and a background regimen consisting of darunavir/r, at least two NRTIs, and optionally the fusion inhibitor enfuvirtide represents a cost-effective option relative to the same darunavir/r-based background regimen in treatment-experienced adults in Canada who have failed prior antiretroviral therapy and who have at least one NNRTI resistance-associated mutation and at least three primary protease inhibitor mutations. The results of our analysis were robust to changes in input parameter values and treatment patterns.
The authors would like to thank Monika Peeters for providing assistance with requests for statistical analyses of clinical trial data.
Conflicts of interest
Funding for this study was provided by Johnson & Johnson Pharmaceutical Services, LLC.
During the conduct of this study, S.M. was an employee of Johnson & Johnson Pharmaceutical Services, LLC. He is currently an employee of Janssen Services, LLC. Both organizations are subsidiaries of Johnson & Johnson. J.M., A.J.B., and S.E.T. are employees of RTI Health Solutions, an independent research organization, and maintained independent scientific control over the study, including data analysis and interpretation of final results. J.M. developed the original structure of the model and was involved in obtaining input data, analyzing the model results, and writing the manuscript. A.J.B. provided input to the model structure, programmed the Markov model, and was involved in obtaining input data, analyzing the model results, and writing the manuscript. S.E.T. provided input to the model programming and was involved in obtaining input data, analyzing the model results, and writing the manuscript. S.M. provided input to the model structure and was involved in obtaining input data and writing the manuscript.
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