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Keywords:

  • Cost-effectiveness;
  • TNF-blocking treatment;
  • Leflunomide

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Objective

To determine the cost effectiveness of treatment strategies for rheumatoid arthritis patients satisfying the indication for tumor necrosis factor (TNF)-blocking treatment.

Methods

A Markov model study was performed. The following treatment strategies were considered: 1) usual treatment; 2) treatment with leflunomide, in the case of nonresponse after 3 months, switch to usual treatment; 3) TNF-blocking treatment, in the case of nonresponse after 3 months, switch to usual treatment; 4) treatment with leflunomide, in the case of nonresponse, switch to TNF-blocking treatment, in the case of nonresponse to TNF-blocking treatment, switch to usual treatment; 5) TNF-blocking treatment, in the case of nonresponse, switch to leflunomide treatment, in the case of nonresponse to leflunomide, switch to usual treatment. Expected patient-years in the different Markov states, costs, and quality-adjusted life years (QALYs) were compared between the treatment strategies; incremental cost-effectiveness ratios (ICERs) were calculated.

Results

Over the 5-year period, the expected effect on disease activity and QALYs was better for treatment strategies that included TNF-blocking treatment than for the other treatment strategies. The greater effectiveness of these treatment strategies reduced medical and nonmedical costs compared with usual treatment by about 16% and 33%, respectively, omitting the costs of medication. When the costs of medication were included, the costs of strategies that started with TNF-blocking treatment were higher than those of the other treatment strategies. Treatment strategy 4 had the most favorable ICER of the treatment strategies that included TNF-blocking treatment: €163,556/QALY compared with usual treatment.

Conclusion

Among strategies that include TNF-blocking agents, one starting with leflunomide and, in the case of nonresponse, switching to TNF-blocking treatment probably results in the most favorable ratio between incremental costs and effects.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Rheumatoid arthritis (RA) is a chronic, inflammatory systemic disease. Its most prominent feature is persistent synovitis in the peripheral joints that can lead to serious morbidity (1, 2). Because there is no definite cure for the disease, the goal of pharmacotherapy is to achieve and maintain low disease activity and to prevent disease progression and the resulting joint destruction and functional disability.

There are several strategies for treating RA (3–5). A usual approach in the Netherlands with newly diagnosed patients is to start with sulfasalazine and, if there is insufficient effect or toxicity, to prescribe methotrexate. If this is also ineffective or leads to major toxicity, a range of other classic disease-modifying antirheumatic drugs (DMARDs) may be used, though they are often only moderately effective and may have considerable toxicity (4). Fortunately, new drugs have recently been introduced, including agents against tumor necrosis factor (TNF) α. TNF-blocking therapy has been effective in patients for whom methotrexate treatment failed (6–8). Another relatively new drug is leflunomide, which has been shown to be at least as effective as methotrexate in the treatment of RA (9).

In the Netherlands, the indication for TNF-blocking treatment is active RA (Disease Activity Score including 28 joints [DAS28] > 3.2) in patients who have been adequately treated with at least 2 DMARDs of which 1 was methotrexate (25 mg/week if tolerated) with insufficient effect. Within 8–12 weeks, an adequate response (an improvement in the DAS28 of ≥1.2) should be present for the therapy to be continued. This is in accordance with a consensus statement of an international group of rheumatologists (10). For leflunomide, a similar indication might apply (9).

The major drawback of these new drugs (especially the TNF-blocking agents) is their high price. Therefore, studies of the cost-effectiveness of treatment strategies including TNF-blocking treatment and leflunomide for the above indication are relevant. Because TNF-blocking treatment and leflunomide have only recently been introduced, no long-term data on costs and effects on quality of life are available. Modeling can be used to overcome the problem of incomplete data and to extrapolate short-term endpoints to long-term outcomes. Modeling involves mathematically combining (empirical) data from different sources (11, 12).

Because RA is a chronic disease characterized by periods of high disease activity alternating with periods of low disease activity, simple decision analysis, which considers outcomes only at 1 time point (instead of outcomes over time), does not suffice. A Markov model considers outcomes over time and might therefore be an appropriate model for RA (13).

In a Markov model, a hypothetical cohort of patients is simulated. The patients are classified in a finite number of health states (Markov states) defined by the severity of the disease. These Markov states can be valued in terms of costs and effects. Development of the disease and the effectiveness of treatment are represented as transitions from one Markov state to another in a defined time span (cycle length). These transitions occur with a certain probability (transition probabilities). According to these transition probabilities, the simulation cohort is distributed over the Markov states, inducing expected costs and expected effects over a certain period (time horizon of the analysis) (11–13).

The aim of this study was to compare the expected effects on disease activity, quality of life, and costs over 5 years of different treatment strategies (including TNF-blocking treatment and leflunomide treatment) in patients who satisfy the indication for TNF-blocking agents in the Netherlands. For this cost-effectiveness study, a societal perspective and a third-party payer perspective were used (11).

METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

A Markov model consisting of Markov states defined by the DAS and a cycle length of 3 months was used (14). Markov states for remission (DAS < 1.6), low disease activity (1.6 < DAS < 2.4), moderate disease activity (2.4 < DAS < 3.7), and high disease activity (DAS28 > 3.7) were defined (15). A time limit of 5 years (20 cycles) was used for the analysis; a longer time horizon implied too many assumptions (Figure 1). Cost and quality of life values were assigned to the Markov states using data from a 48-week multicenter trial with methotrexate that included 411 patients (see Appendix A, available at http://www.interscience.wiley.com/jpages/0004-3591:1/suppmat/index.html). (16). In this trial, medical (consultations physicians, surgery, etc.) and nonmedical (absence from paid labor, travel expenses, etc.) costs were measured using a questionnaire and a patient diary. Costs for drug treatment were excluded because they were partly due to trial medications and were estimated separately (see below). For quality of life valuation, a utility value (derived using the Euroqol questionnaire) was assigned to the Markov states. Utilities refer to the preferences that individuals or society have for a particular health outcome, as measured on a scale between 0 (death) and 1 (perfect health) (11, 12) and can be used to calculate quality-adjusted life years (QALYs). The simulation cohort starts in the Markov state for high disease activity because we considered patients satisfying the current indication for TNF-blocking treatment in the Netherlands, which implies that patients have active disease (Figure 1). In this evaluation, the following treatment strategies were considered: 1) usual treatment (U); 2) treatment with leflunomide, in the case of nonresponse after 3 months, switch to usual treatment (Lef); 3) TNF-blocking treatment, in the case of nonresponse after 3 months, switch to usual treatment (TNFb); 4) treatment with leflunomide, in the case of nonresponse after 3 months, switch to TNF-blocking treatment, in the case of nonresponse to TNF-blocking treatment after 3 months, switch to usual treatment (Lef-TNFb); 5) TNF-blocking treatment, in the case of nonresponse after 3 months, switch to leflunomide treatment, in the case of nonresponse to leflunomide after 3 months, switch to usual treatment (TNFb-Lef). Assessment of response after 3 months of treatment is in accordance with the consensus statement that TNF-blocking treatment should be stopped when a patient does not respond well at 8–16 weeks (10).

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Figure 1. Simulation of a cohort through the Markov model. DAS = Disease Activity Score.

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Costs of drug treatment.

The costs of TNF-blocking treatment and leflunomide in the Netherlands were derived from the College Voor Zorgverzekeringen (Health Care Insurance Board), and were €12,706 per year or €3,177 per cycle for TNF-blocking treatment and €908 per year or €227 per cycle for leflunomide. The cost of usual care for patients who satisfied the indication was calculated from an open longitudinal study of early RA (disease duration <1 year with no prior use of DMARDs) that has been underway since 1985 at the department of Rheumatology of the University Medical Center Nijmegen in the Netherlands. From this study, patients were selected who stopped treatment with sufasalazine and methotrexate due to insufficient effect or toxicity (as noted in the medical record) and had a high disease activity (DAS > 3.7). The followup data from then onward were used to calculate medication use by multiplying the times for which patients used different DMARDs and corticosteriods by the usual dosages. Costs for these treatments were derived using the prices of the drugs. These costs were found to be €150 per year or €38 per cycle.

Effectiveness of treatment strategies.

In a Markov model, the effectiveness of treatment is expressed as transition probabilities, the probability that a transition from one state to another in a defined time span will occur for all transitions. The data needed to calculate these transition probabilities for the different treatments were derived from the following 3 sources.

1) The followup data of patients from the open study who stopped treatment with sulfasalazine and methotrexate due to insufficient effect or toxicity and who had high disease activity were used to calculate the transition probabilities for usual treatment.

2) A dataset made available by Wyeth Pharmaceuticals (Madison, NJ) from clinical trials of monotherapy with etanercept in patients who failed DMARD treatment (1–4 DMARDs) and of combination therapy with methotrexate in patients with insufficient response to methotrexate alone was used (6, 7). From these trials, patients with high disease activity at baseline and a good or moderate response to etanercept (according to the European League Against Rheumatism [EULAR] criteria) after 3 months were selected (15). For the calculation of transition probabilities, data from these 2 trials were combined because they did not differ in efficacy or patient characteristics.

3) Because no detailed data concerning treatment with leflunomide was available, transition probabilities could not be calculated so information from a published clinical trial was used (17). The American College of Rheumatology (ACR) 20%, 50%, and 70% response criteria after 1 and 2 years of treatment, as reported in the article, were considered to represent the Markov states for moderate disease activity, low disease activity, and remission, respectively. It was assumed in the model that the distribution remained constant after 2 years. The patients in the published clinical trial did not satisfy the indication. It seems likely that leflunomide would be less effective when used in patients satisfying the indication. In the analyses, it was therefore arbitrarily assumed that leflunomide treatment is 25% less effective than was reported in the published clinical trial.

Economic evaluation.

For each of the treatment strategies, a specific Markov model was used with the same structure (Figure 1) and the same cost and utility values of the Markov states, but the models used specific transition probabilities and costs for the drug treatments. Using these models, the expected costs and expected effects (outcomes of the model) were compared between the alternative treatment strategies.

Baseline analysis.

The decision model was calculated with the simulation cohort using the point estimates for the transition probabilities and the cost and utility values of the Markov states. Expected patient-years spent in the Markov states (disease activity), cost (medical as well as total), and QALYs over 5 years were compared between the treatment strategies. Cost-effectiveness ratios and incremental cost-effectiveness ratios (ICERs) were calculated. To calculate a cost-effectiveness ratio, the costs of a treatment are divided by the effect of this treatment. To calculate an ICER, the additional costs of a treatment as compared with an alternative are divided by the additional effect as compared with that of the alternative (11, 18). The primary outcome measures of the economic evaluation were the ICERs for the comparisons of the treatment strategies using all costs (medical as well as nonmedical) and QALYs as effectiveness measure. Discounting with 4% per year for the cost as well as the effects was used.

Uncertainty analyses.

Model uncertainty was explored using probabilistic sensitivity analysis. In a probabilistic sensitivity analysis, uncertainties in all input parameters (i.e., transition probabilities, cost, and utility values of the Markov states) of the model are considered simultaneously (19). Instead of a point estimate, a distribution (representing the uncertainty) is defined for all input parameters. When the decision model is calculated, the simulation cohort is simulated through the model repeatedly, and every time the cohort is simulated, a value from the distributions is used (with the chance depending on the distribution) for all input parameters. In this way, the expected outcomes of the model are also represented by distributions, representing the uncertainty of the outcomes.

Distributions were specified for the transition probabilities, the cost, and utility values of the Markov states and for the response of etanercept (EULAR good/moderate) and leflunomide treatment (ACR 20%) after 3 months, and were based on the data. The effectiveness of leflunomide was varied from 50% less effective to equally effective compared with the extrapolation from the effectiveness reported in the published clinical trial, using a uniform distribution. The simulation cohort was simulated 1,000 times. To determine the relative importance of the uncertainty in the different input parameters for the primary outcome measure, correlations between the input parameters and the outcome were calculated in this analysis. Important model parameters as defined by this correlation were varied in a one-way sensitivity analysis.

The Markov models were built using the spreadsheet program Microsoft Excel (Redmond, WA); the program Crystal Ball (version 4.0, Decisioneering, Denver, CO) was used for the probabilistic, sensitivity analysis.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

The patient characteristics of the datasets used to derive the cost and utility values of the Markov states and to derive the transition probabilities were generally similar (Table 1). Exceptions were that disease duration was significantly higher in the population treated with etanercept, and the number of DMARDs used was lower in the population for the valuation of the Markov states. The population treated with leflunomide differed from the other populations in the number of patients with a positive rheumatoid factor test, disease duration, and the number of DMARDs used previously (Table 1).

Table 1. Baseline characteristics of the patient populations for the input data*
 Valuation data n = 411Treatment
Usual n = 26TNF-block n = 92Leflunomide n = 190
  • *

    TNF-block = tumor necrosis factor blocker; RF-pos = rheumatoid factor positive; DMARDs = disease-modifying antirheumatic drugs; DAS = Disease Activity Score.

  • From reference 19.

  • From the open study, patients were selected who had used at least sulfasalazine and methotrexate and who stopped their use due to insufficient effect or toxicity.

  • §

    Calculated from data from the published trial.

Age, mean ± SD years56.5 ± 12.756.5 ± 12.550.8 ± 11.454 ± 12
Female, n (%)290 (70.6)20 (83.3)76 (82.6)138 (72.5)
RF-pos, n (%)318 (81.0)20 (90.9)78 (84.7)123 (64.8)
Disease duration, mean ± SD years6.5 ± 7.63.8 ± 2.111.1 ± 7.37.0 ± 8.6
No. of DMARDs used, mean ± SD1.5 ± 1.1≥23.0 ± 1.40.8 ± 1.0
DAS, mean ± SD4.6 ± 0.944.5 ± 0.65.4 ± 1.04.8§

Results of economic evaluation.

Results of baseline analysis.

Table 2 shows the expected percentage of time medications were used in the different treatment strategies. In the models for strategies including etanercept (TNFb, Lef-TNFb, TNFb-Lef), more than twice as long was spent in the Markov state for remission compared with usual treatment and 1.5–2 times less time was spent in the Markov state for high disease activity. The model for leflunomide treatment was intermediate between etanercept and usual treatment in these respects (Table 2). In Figure 2, the distribution of the simulation cohort over the Markov states per cycle is shown for the different treatment strategies (without discounting). After about the ninth cycle, an equilibrium is reached between the transitions from and to the Markov states for higher disease activity in all treatment strategies. On a group level, the distribution over the Markov states therefore remains constant, although transitions between the states still occur.

Table 2. Treatment, patient-years in the Markov states, QALYs, costs, and CER over 5 years for each treatment strategy*
OutcomeUsualLefLef-TNFbTNFbTNFb-Lef
  • *

    QALYs = quality-adjusted life years; CER = cost-effectiveness ratio; Lef = leflunomide; TNFb = tumor necrosis factor blocking; DAS = Disease Activity Score; Mod = moderate; RA = rheumatoid arthritis; U = usual.

  • Costs and effects are discounted 4% per year.

  • A CER is the costs of a treatment divided by the effect of this treatment. To calculate an incremental cost-effectiveness ratio (ICER), the additional costs of a treatment as compared with an alternative are divided by the additional effect as compared with that of the alternative.

Treatment, % of time     
 Usual10048.87.615.67.6
 Leflunomide051.251.208.0
 TNF blocking0041.284.484.4
Patient years in Markov states     
 Remission0.410.730.950.880.93
 Low DAS0.720.871.031.081.11
 Mod DAS1.471.301.341.551.52
 High DAS1.971.671.241.051.00
Costs (€)     
 Medical (excluding RA-medication)5,1844,7864,4084,3644,303
 RA medication6852,54725,08549,52549,799
 Nonmedical7,3436,0415,5455,0684,935
 Total13,21213,92135,03858,97559,037
QALYs2.862.932.993.003.01
CER, €/QALY4.6204.75111.70319.65819.588
  Lef vs ULef-TNFb vs LefTNFb vs Lef-TNFbTNFb-Lef vs Lef-TNFb
ICER €/QALY 10.584317.627Extended Dominance1.155.314
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Figure 2. Distribution of the simulation cohort over the Markov states during 5 years (20 cycles, time horizon) for the different treatment strategies. TNF = tumor necrosis factor; das = Disease Activity Score; mod = moderate.

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The most effective treatment strategies in terms of the expected number of QALYs were also the treatment strategies using etanercept (TNFb, Lef-TNFb, TNFb-Lef; Table 2). And without the costs for drug treatment, the expected medical and total costs were lowest for these treatment strategies. The differences in the number of QALYs and the costs between these strategies were small. Compared with usual treatment, cost reductions of 16% and 33% (in medical and nonmedical costs, respectively) occurred with treatment strategy TNFb-Lef. When the costs for the drug treatment were also considered, the expected costs for the treatment strategies starting with etanercept (TNFb, TNFb-Lef) were higher than the expected costs for the other treatment strategies. Leflunomide treatment costs about the same as usual treatment, and the cost of Lef-TNFb is intermediate between that of treatment strategies starting with TNF-blocking treatment and that of leflunomide treatment. In Table 2, the ICERs for each consecutive comparison from the least effective treatment (in terms of QALYs) to the most effective are shown. The ICER for strategy TNFb compared with Lef-TNFb was very high and even higher than the ICER for strategy TNFb-Lef (the most effective strategy) compared with Lef-TNFb. This phenomenon is called extended dominance (20) and is the reason why treatment strategy TNFb is not shown in Table 3.

Table 3. Incremental cost-effectiveness ratios for the comparisons between the treatment strategies using medical as well as total costs*
OutcomeLef vs ULef-TNFb vs ULef-TNFb vs LefTNFb-Lef vs UTNFb-Lef vs LefTNFb-Lef vs Lef-TNFb
  • *

    Because strategy TNFb is dominated (extended dominance) this strategy is not included in the table. Lef = leflunomide; vs = versus; U = usual treatment; TNFb = tumor necrosis factor blocking; QALY = quality-adjusted life year; DAS28 = Disease Activity Score in 28 joints; patyr = patient-year.

  • Costs and effects are discounted 4% per year

  • Strategy TNFb-Lef is inferior to strategy Lef-TNFb.

Medical costs      
 Utility (€/QALY)21,866177,037333,323312,768536,0061,184,659
 DAS28 < 1.6 (€/patyr)4,54643,867102,36692,804236,597−1,308,869
 DAS28 < 2.4 (€/patyr)3,15227,83757,69553,420106,696453,630
 DAS28 < 3.7 (€/patyr)4,90532,70552,28250,11170,431102,456
All costs      
 Utility (€/QALY)10,584163,556317,627297,151517,0611,155,314
 DAS28 < 1.6 (€/patyr)2,20140,52797,54688,170228,234−1,276,447
 DAS28 < 2.4 (€/patyr)1,52625,71754,97850,753102,925442,393
 DAS28 < 3.7 (€/patyr)2,37430,21549,82047,60967,94199,918

Table 3 shows the comparisons between the treatment strategies. The ICERs using patient-years spent in the Markov states are also shown.

Results of uncertainty analysis.

Table 4 shows the uncertainty of the expected outcomes of the different treatment strategies, as calculated in the probabilistic sensitivity analysis. The ranges in the number of QALYs for the different treatment strategies overlap, and the ranges in costs overlap for usual treatment and leflunomide treatment and for treatment strategies TNFb and TNFb-Lef. Therefore, in theory it is possible that the treatment strategies with etanercept are inferior (less effective and more expensive) to usual treatment, leflunomide treatment, or each other. The median ICERs were all close to the ICERs from the baseline analysis. In 16% of simulations, leflunomide treatment was dominant (more effective and less expensive) and in 2% inferior (less effective and more expensive) compared with usual treatment. In <1% of simulations, strategies TNFb-Lef and Lef-TNFb were inferior to usual treatment or leflunomide treatment. The strategy TNFb was often dominated by TNFb-Lef and Lef-TNFb and almost never dominated any strategy and was therefore not further considered.

Table 4. Medical and total costs (€) and number of QALYs per patient over 5 years. Results of probabilistic sensitivity analysis*
OutcomeULefTNFbLef-TNFbTNFb-Lef
  • *

    Data presented as mean (2.5–97.5 percentile). QALYs = quality-adjusted life years; U = usual treatment; Lef = leflunomide; TNFb = tumor necrosis factor blocking.

  • Discounted by 4% per year.

  • The effectiveness of leflunomide treatment was varied from 50% less effective to equally effective as in the original article (reference 19). The figures represent the mean of the cost and QALYs from all simulation in the probabilistic sensitivity analysis and the 2.5 and 97.5 percentile of these simulations.

Medical costs5,892 (4,277–7,642)7,366 (5,793–8,804)53,890 (49,183–57,904)29,714 (24,573–34,938)54,027 (49,373–57,844)
Total costs13,319 (9,270–17,659)14,160 (10,926–17,826)58,842 (53,663–63,323)35,345 (29,216–41,500)58,948 (54,291–63,406)
Total no. of QALYs2.88 (2.75–2.99)2.94 (2.83–3.05)3.03 (2.93–3.14)3.01 (2.91–3.11)3.04 (2.93–3.15)

In Figure 3, the differences in total costs are compared with the differences in effects (QALYs) in a so-called “cost-effectiveness plane” to illustrate the ratio between the extra costs and the extra effects of a treatment strategy as compared with another treatment strategy (11). Figure 3A shows that strategy Lef-TNFb and TNFb-Lef overlap concerning the extra effects as compared with usual treatment, but that strategy TNFb-Lef always implies more addtional costs. Figure 3B shows that strategy Lef-TNFb and strategy TNFb-Lef overlap in effectiveness, but that strategy TNFb-Lef is always more expensive. Furthermore it shows that in 13% of simulations, Lef-TNFb is more effective than TNFb-Lef and in the rest of the simulations TNFb-Lef is more effective but the differences between the treatments are always small.

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Figure 3. A, Cost-effectiveness plane for the comparison between strategy leflunomide—tumor necrosis factor blocking (Lef-TNFb) and usual treatment, and between tumor necrosis factor blocking-leflunomide (TNFb-Lef) and usual treatment. B, Cost-effectiveness plane for the comparison between strategy Lef-TNFb and TNFb-Lef. Results of probabilistic sensitivity analysis. X-axis = incremental effects (difference in quality-adjusted life years [QALYs]); y-axis = incremental costs (difference in costs).

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The effectiveness of the treatments and the values of the Markov states in terms of utility, in particular, determined the ICERs. The correlation coefficients for these factors with the ICERs for the different comparisons varied from 0.08 to 0.55 for the treatment effectiveness parameters and from 0.08 to 0.30 for the utility values. The other input parameters were less important, with all correlation coefficients <0.10. Varying these important input parameters did not lead to different results from the probabilistic sensitivity analysis. Also varying the costs of the drug treatment itself (from 33% less costly to 33% more costly) did not have a major influence on these results.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

In this modeling study it was found that, over a period of 5 years, the expected effect of treatment strategies that include etanercept on disease activity and QALYs is larger than the expected effect of usual treatment or leflunomide treatment in RA patients satisfying the indication for TNF-blocking agents in the Netherlands. This reduces medical and nonmedical costs by about 16% and 33%, respectively, as compared with usual treatment; this does not, however, counter-balance the extra costs of the drug treatment for the treatment strategies starting with etanercept. When these costs are included, the extra costs for the strategies starting with etanercept are considerable, leading to large (incremental) cost-effectiveness ratios.

The strategy of starting with leflunomide and, in case of nonresponse, starting etanercept seems to have an effect similar to that of treatment strategies starting with etanercept, but the total costs are lower. This results in more acceptable ICERs for the comparison with usual treatment or leflunomide treatment alone. The ICERs for the different comparisons were sensitive to the effectiveness of the treatments, and the utility values of the Markov states, but the strategies using etanercept were almost always more effective and the strategies starting with etanercept were always more expensive.

This study has some deficiencies. The first of these relates to the populations used to derive efficacy data for the treatments. The indication for TNF-blocking treatment in the Netherlands is patients with a DAS28 > 3.2 who have been adequately treated with at least 2 DMARDs. This relatively low level of disease activity was chosen to present rheumatologists with the opportunity to prescribe TNF-blocking treatment in individual patients with a lower DAS. However in clinical practice and in clinical trials, patients generally have a DAS > 3.7. Therefore for this study, a DAS28 > 3.7 was chosen as the definition of active disease. Although all patients had active disease, the requirement of DMARD treatment may not have been satisfied for all patients because some patients had not been treated with a dosage of methotrexate that is currently considered adequate before stopping the treatment (for the open study and the clinical trial with etanercept), and some patients may not have taken at least 2 DMARDs that resulted in failure (in the clinical trials with etanercept). However, we selected patients such that most of them would meet the inclusion criteria for TNF-blocking treatment in the Netherlands. Although patients from the leflunomide trial did not satisfy the indication and no selection was possible because we did not have detailed data, we assumed leflunomide to be 25% less effective with this indication. Varying this decrease in effect, in a separate analysis, showed that leflunomide would have to be more than 90% less effective for the ICER to be below €100,000/QALY for the comparison of TNFb-Lef versus Lef-TNFb, which is not probable. For the effectiveness of leflunomide, an extrapolation of the ACR 20%, 50%, and 70% response criteria to the Markov states for moderate disease activity, low disease activity, and remission, respectively, was used. This probably leads to an underestimation of effectiveness and is thus a conservative estimate (21).

A second concern relates to the fact that this study is not a direct comparison between treatment strategies but a modeling study. Before using the model for this cost-effectiveness study, it was validated according to a method proposed by McCabe and Dixon (22) and found to be valid for use in economic evaluations in RA (14). Disease activity was found to be the most important factor determining the cost and utility values for the Markov states, and the toxicity of the methotrexate treatment did not influence these values, using data from the methotrexate trial (14, 22). No clear relationship of costs with disease duration was found. This indicated that the data from the trial with methotrexate could be used to calculate the cost and utility values for the Markov states in our context. Furthermore, it was found that transition probabilities calculated from data from the open inception cohort could be extrapolated to 5 years. The expected distribution of the patients over the Markov states (results of the model) resembled the actual data fairly well (14). In modeling, assumptions are unavoidable, but they should be made realistically, or even conservatively, for the results to be credible. Extensive sensitivity analyses are necessary to judge whether the results and conclusions can be regarded as robust. This is one of the main advantages of modeling (12). An assumption in our model was that the costs for drug treatment were equal for all Markov states. This assumption is valid because patients do not usually stop taking or change their medication when they go into remission or low disease activity. Use of nonsteroidal antiinflammatory drugs (NSAIDs) was not included in the costs of drug treatment. However, large differences in NSAID use are not expected between the different treatment strategies. A further assumption in this model was that when a patient responds well to treatment with TNF-blocking treatment or leflunomide treatment, this treatment would be continued for the rest of the 5 years. The percentage of patients with a response after 3 months was varied in the sensitivity analyses and did not have a large influence.

The time horizon for our analyses was 5 years. It is possible that there would be additional savings from the use of TNF-blocking treatment (such as prevention of surgical procedures, admission to nursing homes, etc.) outside the 5-year time horizon of the analyses. These savings are uncertain and rely on the long-term effects of TNF-blocking treatment on disease activity and toxicity. Recently, reference case recommendations for economic evaluations in rheumatoid arthritis were published by the Outcome Measures in Rheumatology Clinical Trials group (23). Our study is in accordance with almost all of the recommendations. Only mortality was not accounted for in this study, however it is not expected that over the study horizon of 5 years, a difference in mortality is apparent between the treatment strategies.

Notwithstanding the limitations of the study we think the results of this analysis are valid because the extensive sensitivity analyses did not change the conclusions of the study.

In this study, the cost effectiveness of treatment strategies using etanercept was investigated. Etanercept was used merely as an example of TNF-blocking treatment. It is not clear that the drugs differ in effectiveness regarding RA and Infliximab, for example, is probably even more expensive due to the mode of administration. We therefore consider the conclusions of this study to be representative for other TNF-blocking agents. Extrapolating these findings to other countries or comparing them with results for other countries should be done with caution because the analyses and the cost values were specific to the situation in the Netherlands.

Few studies have investigated the cost effectiveness of treatment strategies for a patient population as was done in this study. Choi et al (24) compared treatment options for methotrexate-resistant RA. In this study, a reference analysis of methotrexate therapy for methotrexate-naive RA compared with no treatment, costing $1,100/ACR 20% response, was used to compare cost-effectiveness ratios. The authors concluded that triple therapy (hydroxychloroquine, sulfasalazine, and methotrexate) is probably cost effective in this patient group because this costs only 1.3 times more per patient ACR 20% outcome, and etanercept in combination with methotrexate had a high cost-effectiveness ratio of $42,600/ACR 20% response (38 times more compared with the reference case). The other treatment options (continuing methotrexate, etanercept monotherapy, cyclosporine plus methotrexate, and no second-line agent) were dominated by methotrexate therapy. This study had a time horizon of only 6 months and did not use non–disease-specific outcome measures, such as utility, for comparison with cost-effectiveness studies in other diseases. Leflunomide treatment was not considered in this cost-effectiveness study. In our study, triple therapy was not one of the comparators because this kind of therapy is not often used in the Netherlands.

Wong et al (25) estimated the cost-effectiveness of 54 weeks of infliximab as compared with methotrexate treatment in patients that had active disease despite treatment with methotrexate. They projected the 54-week results from the Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group (ATTRACT) into lifetime economic and clinical outcomes using a Markov model with Markov states defined by the health assessment questionnaire (HAQ). Because the effectiveness of infliximab beyond the 54 weeks was not available, they assumed that infliximab would be discontinued and the patients would start methotrexate treatment. This assumption is probably not valid and results in an underestimation of the ICER. Furthermore, infliximab treatment was compared with methotrexate treatment in a group of patients that did not respond well to treatment; this is probably not the best available alternative or the usual treatment for these patients. The authors concluded that 54 weeks of infliximab treatment should be cost effective.

Recently, Brennan et al compared a DMARD sequence with etanercept as a third line to a DMARD sequence excluding etanercept in RA patients that did not get improvement from 2 DMARDs (26). They calculated lifetime economic and quality of life outcomes using a decision model that focused on the progression of HAQ disability and simulates individual patient histories according to the chance of responding to treatment (ACR 20%), adverse event, and death. Although they found a low ICER (£16,330 per QALY) as compared with our study, the results are difficult to compare because of the different modeling approach.

We found 2 studies (27, 28) that investigated the costs of RA care in patients who started treatment with leflunomide as compared with etanercept (and infliximab) (28). These studies concluded that RA care costs in the leflunomide group were lower. The treatments were not compared on effectiveness, however, and one of the studies was retrospective (27). The costs were based on databases containing patient-level medical and pharmaceutical claims. It is unclear whether the patient populations of these 2 studies are comparable with the patient population used in our study.

One study compared a DMARD sequence including leflunomide treatment with a DMARD sequence without leflunomide in patients with RA severe enough to require treatment with methotrexate (29). They used a decision model in which patients could respond to therapy or not (ACR 20%), continue therapy, stop therapy, and experience adverse events. The time horizon of the analyses was 5 years. It was concluded that adding leflunomide as a new option to a conventional sequence of DMARDs was reasonably cost effective, with a ICER of $54,229 or $71,988 per QALY gained, depending on the method of calculating QALYs. The population of this study was not comparable to the patients in our study.

We conclude that treatment strategies including TNF-blocking treatment are probably the most effective for patients for whom 2 DMARDs have failed, of which 1 is methotrexate. From these strategies, a strategy starting with leflunomide, and in the case of nonresponse, switching to TNF-blocking treatment, probably results in the most favorable ratio between the extra costs and effects.

We would like to recommend that in addition to the current indication for TNF-blocking treatment in the Netherlands, unless it is contraindicated, leflunomide should also be used for at least 3 months, with insufficient effect, before starting TNF-blocking treatment.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

The authors would like to acknowledge Wyeth Pharmaceuticals for making available clinical trial data concerning etanercept.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information
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